Search results for: tumor image

Authors: Abdulkader Helwan

Abstract:

Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Sehreen Moorat, Mussarat Lakho

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A method of artificial intelligence using digital mammograms data has been proposed in this paper for detection of breast cancer. Many researchers have developed techniques for the early detection of breast cancer; the early diagnosis helps to save many lives. The detection of breast cancer through mammography is effective method which detects the cancer before it is felt and increases the survival rate. In this paper, we have purposed image processing technique for enhancing the image to detect the graphical table data and markings. Texture features based on Gray-Level Co-Occurrence Matrix and intensity based features are extracted from the selected region. For classification purpose, neural network based supervised classifier system has been used which can discriminate between benign and malignant. Hence, 68 digital mammograms have been used to train the classifier. The obtained result proved that automated detection of breast cancer is beneficial for early diagnosis and increases the survival rates of breast cancer patients. The proposed system will help radiologist in the better interpretation of breast cancer.

Keywords: medical imaging, cancer, processing, neural network

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Authors: M. Dasgupta, S. Banerjee

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Content Based Image Retrieval (CBIR) coupled with Case Based Reasoning (CBR) is a paradigm that is becoming increasingly popular in the diagnosis and therapy planning of medical ailments utilizing the digital content of medical images. This paper presents a survey of some of the promising approaches used in the detection of abnormalities in retina images as well in mammographic screening and detection of regions of interest in MRI scans of the brain. We also describe our proposed algorithm to detect hard exudates in fundus images of the retina of Diabetic Retinopathy patients.

Keywords: case based reasoning, exudates, retina image, similarity based retrieval

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Authors: Shima Behzadi

Abstract:

Background: In many human malignant tumors, microRNA expression is aberrant. This study investigates miR-141 as a prognostic marker in colorectal cancer with K-Ras mutation. Materials and methods: In this case-control study, 100 patients, mostly over the age of 50, who were diagnosed with colorectal cancer were selected. The pathology department of the Mostoufi Pathobiology and Genetics Laboratory in Tehran confirmed the presence of colorectal cancer in samples of paraffin-embedded colon tissue. The case group was composed of patients with codon 12 and 13 mutations in exon 2 of the K-Ras gene, while tumor samples of individuals without these mutations in exon 2 of the K-Ras gene were selected as the control group, with patient consent. The changes in the expression of miR-141 were examined in both groups. Results: The study found that 20% of the patients tested positive for codon 12 mutation, and 10% of patients had codon 13 mutation. As a result, in 30 cases, there was a higher level of miR-141 expression. The miR-141 gene expression level in K-Ras positive tumor samples was 1.5 times higher than its expression level in K-Ras negative samples. This increase in expression was statistically significant, with a p-value of less than 0.001, indicating that the observed results are highly statistically significant. Conclusion: The study revealed that the incidence of typical K-Ras gene mutations among the colorectal cancer patients in the sample matches the national average in Iran. Additionally, the expression of miR-141 can serve as a useful biomarker to aid in the prognosis of colorectal cancer.

Keywords: colorectal cancer, K-Ras gene, miR-141 marker, real time PCR, electrophoresis

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Authors: Tudor Barbu

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We present a robust nonlinear parabolic partial differential equation (PDE)-based denoising scheme in this article. Our approach is based on a second-order anisotropic diffusion model that is described first. Then, a consistent and explicit numerical approximation algorithm is constructed for this continuous model by using the finite-difference method. Finally, our restoration experiments and method comparison, which prove the effectiveness of this proposed technique, are discussed in this paper.

Keywords: anisotropic diffusion, finite differences, image denoising and restoration, nonlinear PDE model, anisotropic diffusion, numerical approximation schemes

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Authors: F. Rarbi, D. Dzahini, W. Uhring

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In this paper, a dynamic and power efficient 8-bit and 100-MSPS Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) is presented. The circuit uses a non-differential capacitive Digital-to-Analog (DAC) architecture segmented by 2. The prototype is produced in a commercial 65-nm 1P7M CMOS technology with 1.2-V supply voltage. The size of the core ADC is 208.6 x 103.6 µm². The post-layout noise simulation results feature a SNR of 46.9 dB at Nyquist frequency, which means an effective number of bit (ENOB) of 7.5-b. The total power consumption of this SAR ADC is only 1.55 mW at 100-MSPS. It achieves then a figure of merit of 85.6 fJ/step.

Keywords: CMOS analog to digital converter, dynamic comparator, image sensor application, successive approximation register

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Authors: Mustafa Cam, Nedim Ongun, Ufuk Kutluana

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Introduction: Restless LegsSyndrome (RLS) is a common, under-recognized disorder disrupts sleep and diminishes quality of life (1). The most common conditions highly associated with RLS include renalfailure, iron and folic acid deficiency, peripheral neuropathy, pregnancy, celiacdisease, Crohn’sdiseaseandrarelymalignancy (2).Despite a clear relation between low peripheral iron and increased prevalence and severity of RLS, the prevalence and clinical significance of RLS in iron-deficientanemic populations is unknown (2). We report here a case of RLS due to iron deficiency in the setting of neuroendocrinetumor. Report of Case: A 35 year-old man was referred to our clinic with general weakness, weight loss (10 kg in 2 months)and 2-month history of uncomfortable sensations in his legs with urge to move, partially relieved by movement. The symptoms were presented very day, worsening in the evening; the discomfort forced the patient to getup and walk around at night. RLS was severe, with a score of 22 at the International RLS ratingscale. The patient had no past medical history. The patient underwent a complete set of blood analyses and the following ab normal values were found (normal limitswithinbrackets): hemoglobin 9.9 g/dl (14-18), MCV 70 fL (80-94), ferritin 3,5 ng/mL (13-150). Brain and spinemagnetic resonance imaging was normal. The patient consultated with gastroenterology clinic and gastointestinal systemendoscopy was performed for theetiology of the iron deficiency anemia. After the gastricbiopsy, results allowed us to reach the diagnosis of neuroen docrine tumor and the patient referred to oncology clinic. Discussion: The first important consideration from this case report is that the patient was referred to our clinic because of his severe RLS symptoms dramatically reducing his quality of life. However, our clinical study clearly demonstrated that RLS was not the primary disease. Considering the information available for this patient, we believe that the most likely possibility is that RLS was secondary to iron deficiency, a very well-known and established cause of RLS in theliterature (3,4). Neuroendocrine tumors (NETs) are rare epithelial neoplasms with neuroendocrine differentiation that most commonly originate in the lungs and gastrointestinal tract (5). NETs vary widely in their clinical presentation; symptoms are often nonspecific and can be mistaken for those of other more common conditions (6). 50% of patients with reported disease stage have either regional or distant metastases at diagnosis (7). Accurate and earlier NET diagnosis is the first step in shortening the time to optimal care and improved outcomes for patients (8). The most important message from this case report is that RLS symptoms can sometimes be thesign of a life-threatening condition. Conclusion: Careful and complete collection of clinical and laboratory data should be carried out in RLS patients. Inparticular, if RLS onset coincides with weight loss and iron deficieny anemia, gastricendos copy should be performed. It is known about that malignancy is a rare etiology in RLS patients and to our knowledge; it is the first case with neuro endocrine tumor presenting with RLS.

Keywords: neurology, neuroendocrine tumor, restless legs syndrome, sleep

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Authors: B. O. Olawale, C. R. Chatwin, R. C. D. Young, P. M. Birch, F. O. Faithpraise, A. O. Olukiran

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Ortho-rectification is the process of geometrically correcting an aerial image such that the scale is uniform. The ortho-image formed from the process is corrected for lens distortion, topographic relief, and camera tilt. This can be used to measure true distances, because it is an accurate representation of the Earth’s surface. Ortho-rectification and geo-referencing are essential to pin point the exact location of targets in video imagery acquired at the UAV platform. This can only be achieved by comparing such video imagery with an existing digital map. However, it is only when the image is ortho-rectified with the same co-ordinate system as an existing map that such a comparison is possible. The video image sequences from the UAV platform must be geo-registered, that is, each video frame must carry the necessary camera information before performing the ortho-rectification process. Each rectified image frame can then be mosaicked together to form a seamless image map covering the selected area. This can then be used for comparison with an existing map for geo-referencing. In this paper, we present a four-step ortho-rectification procedure for real-time geo-referencing of video data from a low-cost UAV equipped with multi-sensor system. The basic procedures for the real-time ortho-rectification are: (1) Decompilation of video stream into individual frames; (2) Finding of interior camera orientation parameters; (3) Finding the relative exterior orientation parameters for each video frames with respect to each other; (4) Finding the absolute exterior orientation parameters, using self-calibration adjustment with the aid of a mathematical model. Each ortho-rectified video frame is then mosaicked together to produce a 2-D planimetric mapping, which can be compared with a well referenced existing digital map for the purpose of georeferencing and aerial surveillance. A test field located in Abuja, Nigeria was used for testing our method. Fifteen minutes video and telemetry data were collected using the UAV and the data collected were processed using the four-step ortho-rectification procedure. The results demonstrated that the geometric measurement of the control field from ortho-images are more reliable than those from original perspective photographs when used to pin point the exact location of targets on the video imagery acquired by the UAV. The 2-D planimetric accuracy when compared with the 6 control points measured by a GPS receiver is between 3 to 5 meters.

Keywords: geo-referencing, ortho-rectification, video frame, self-calibration

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

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Authors: Felix Givois, Nicolas R. Gauger, Matthias Kabel

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The efficient digital material characterization is of great interest to many fields of application. It consists of the following three steps. First, a 3D reconstruction of 2D scans must be performed. Then, the resulting gray-value image of the material sample is enhanced by image processing methods. Finally, partial differential equations (PDE) are solved on the segmented image, and by averaging the resulting solutions fields, effective properties like stiffness or conductivity can be computed. Due to the high resolution of current CT images, the latter is typically performed with matrix-free solvers. Among them, a solver that uses the explicit formula of the Green-Eshelby operator in Fourier space has been proposed by Moulinec and Suquet. Its algorithmic, most complex part is the Fast Fourier Transformation (FFT). In our talk, we will discuss the potential quantum advantage that can be obtained by replacing the FFT with the Quantum Fourier Transformation (QFT). We will especially show that the data transfer for noisy intermediate-scale quantum (NISQ) devices can be improved by using appropriate boundary conditions for the PDE, which also allows using semi-classical versions of the QFT. In the end, we will compare the results of the QFT-based algorithm for simple geometries with the results of the FFT-based homogenization method.

Keywords: most likelihood amplitude estimation (MLQAE), numerical homogenization, quantum Fourier transformation (QFT), NISQ devises

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Authors: Hossain A., Miah S., Ray P. K.

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Introduction: Single channel and tri-channel applicators are used in the traditional treatment of cervical cancer. Total reference air kerma (TRAK) and treatment outcomes in high-dose-rate brachytherapy for cervical cancer using single-channel and tri-channel applicators were the main objectives of this retrospective study. Material and Methods: Patients in the radiotherapy division who received brachytherapy, chemotherapy, and external radiotherapy (EBRT) using single and tri-channel applicators were the subjects of a retrospective cohort study from 2016 to 2020. All brachytherapy parameters, including TRAK, were calculated in accordance with the international protocol. The Kaplan Meier method was used to analyze survival rates using a log-rank test. Results and Discussions: Based on treatment times of 15.34 (10-20) days and 21.35 (6.5-28) days, the TRAK for the tri-channel applicator was 0.52 cGy.m² and for the single-channel applicator was 0.34 cGy.m². Based on TRAK, the rectum, bladder, and tumor had respective Pearson correlations of 0.082, 0.009, and 0.032. The 1-specificity and sensitivity were 0.70 and 0.30, respectively. At that time, AUC was 0.71. The log-rank test showed that tri-channel applicators had a survival rate of 95% and single-channel applicators had a survival rate of 85% (p=0.565). Conclusions: The relationship between TRAK and treatment duration and Pearson correlation for the tumor, rectum, and bladder suggests that TRAK should be taken into account for the proper operation of single channel and tri-channel applicators.

Keywords: single-channel, tri-channel, high dose rate brachytherapy, cervical cancer

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Authors: O. Abusaeeda, J. P. O. Evans, D. Downes

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We demonstrate the synthesis of intermediary views within a sequence of X-ray images that exhibit depth from motion or kinetic depth effect in a visual display. Each synthetic image replaces the requirement for a linear X-ray detector array during the image acquisition process. Scale invariant feature transform, SIFT, in combination with epipolar morphing is employed to produce synthetic imagery. Comparison between synthetic and ground truth images is reported to quantify the performance of the approach. Our work is a key aspect in the development of a 3D imaging modality for the screening of luggage at airport checkpoints. This programme of research is in collaboration with the UK Home Office and the US Dept. of Homeland Security.

Keywords: X-ray, kinetic depth, KDE, view synthesis

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Authors: Balraju Vadlakonda, Narasimha Mangadoddy

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The hydrodynamic behavior in a laboratory column flotation was analyzed using particle image velocimetry. For complete characterization of column flotation, it is necessary to determine the flow velocity induced by bubbles in the liquid phase, the bubble velocity and bubble characteristics:diameter,shape and bubble size distribution. An experimental procedure for analyzing simultaneous, phase-separated velocity measurements in two-phase flows was introduced. The non-invasive PIV technique has used to quantify the instantaneous flow field, as well as the time averaged flow patterns in selected planes of the column. Using the novel particle velocimetry (PIV) technique by the combination of fluorescent tracer particles, shadowgraphy and digital phase separation with masking technique measured the bubble velocity as well as the Reynolds stresses in the column. Axial and radial mean velocities as well as fluctuating components were determined for both phases by averaging the sufficient number of double images. Bubble size distribution was cross validated with high speed video camera. Average turbulent kinetic energy of bubble were analyzed. Different air flow rates were considered in the experiments.

Keywords: particle image velocimetry (PIV), bubble velocity, bubble diameter, turbulent kinetic energy

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Authors: Navkiran Kaur, Apoorva Mathur, Abhishree Agarwal, Sakshi Gupta, Tuhin Rashmi

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Aim: Breast cancer is the most common cancer in women worldwide, and resistance to the current therapeutics, often concurrently, is an increasing clinical challenge. Glycosylation of proteins is one of the most important post-translational modifications. It is widely known that aberrant glycosylation has been implicated in many different diseases due to changes associated with biological function and protein folding. Alterations in cell surface glycosylation, can promote invasive behavior of tumor cells that ultimately lead to the progression of cancer. In breast cancer, there is an increasing evidence pertaining to the role of glycosylation in tumor formation and metastasis. In the present study, an attempt has been made to study the disease associated sialoglycoproteins in breast cancer by using bioinformatics tools. The sequence will be retrieved from UniProt database. A database in the form of a word document was made by a collection of FASTA sequences of breast cancer gene sequence. Glycosylation was studied using yinOyang tool on ExPASy and Differential genes expression and protein analysis was done in context of breast cancer metastasis. The number of residues predicted O-glc NAc threshold containing 50 aberrant glycosylation sites or more was detected and recorded for individual sequence. We found that the there is a significant change in the expression profiling of glycosylation patterns of various proteins associated with breast cancer. Differential aberrant glycosylated proteins in breast cancer cells with respect to non-neoplastic cells are an important factor for the overall progression and development of cancer.

Keywords: breast cancer, bioinformatics, cancer, metastasis, glycosylation

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Mei-Yi Wu, Shang-Ming Huang

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The online shoppers nowadays often search the product information on the Internet using some keywords of products. To use this kind of information searching model, shoppers should have a preliminary understanding about their interesting products and choose the correct keywords. However, if the products are first contact (for example, the worn clothes or backpack of passengers which you do not have any idea about the brands), these products cannot be retrieved due to insufficient information. In this paper, we discuss and study the applications in E-commerce using image retrieval and text mining techniques. We design a reasonable E-commerce application system containing three layers in the architecture to provide users product information. The system can automatically search and retrieval similar images and corresponding web pages on Internet according to the target pictures which taken by users. Then text mining techniques are applied to extract important keywords from these retrieval web pages and search the prices on different online shopping stores with these keywords using a web crawler. Finally, the users can obtain the product information including photos and prices of their favorite products. The experiments shows the efficiency of proposed system.

Keywords: mobile image retrieval, text mining, product information service system, online marketing

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

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Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, deep neural networks, CNN, Depth estimation

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

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The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

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Authors: Parveen Kundu, Nitika Chawla, Ruchi Agarwal, Swaran Kaur

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Background: Mature cystic teratoma is a benign, most common tumor of the ovary occurring mostly in young and middle-aged females. This study consists of 19 cases of mature cystic teratomas which were received in the Department Of Pathology over a period of two years. There were malignant transformations observed in three cases, which makes it very important for pathologists to thoroughly examine the entire specimen of mature cystic teratomas. Material and Methods: Nineteen reported cases of mature cystic teratomas were received in Deptt. Of Pathology, BPS GMC Khanpur Kalan, Sonepat, over a two-year period from November 2020 to October 2022 and reviewed retrospectively. Data regarding age, size, laterality, gross, morphological features, and surgery performed were retrieved from pathological archives. Results: In our study, the most common age of presentation was the 20-40 year age group. The most common presenting complaint was fullness in the abdomen or abdominal distension. Four out of 19 cases studied cases presented with bilateral ovarian cysts. Tumor size ranged from 6 to 20 cm in diameter. In seven cases, cysts were greater than or equal to 10 cm in diameter. Three cases showed malignant transformation. Conclusion: It is very important to thoroughly examine the contralateral ovary to rule out bilateral presentation. A furthermost thorough examination is advised in tumors of size >10 cm and in tumors with solid areas to rule out any malignant transformation.

Keywords: teratoma, ovary, malignant, transformation

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Authors: Yali Zheng, Peipei Luo, Shinan Chen, Jiasheng Hao, Hong Cheng

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Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments.

Keywords: indoor navigation, low light, RGB-D camera, vision based

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Authors: Ankit Sharma, Mudit Sachan, Anurag Prakash

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Rapid growth in data acquisition technologies through drones, have led to advances and interests in collecting high-resolution images of geological fields. Being advantageous in capturing high volume of data in short flights, a number of challenges have to overcome for efficient analysis of this data, especially while data acquisition, image interpretation and processing. We introduce a method that allows effective mapping of geological fields using photogrammetric data of surfaces, drainage area, water bodies etc, which will be captured by airborne vehicles like UAVs, we are not taking satellite images because of problems in adequate resolution, time when it is captured may be 1 yr back, availability problem, difficult to capture exact image, then night vision etc. This method includes advanced automated image interpretation technology and human data interaction to model structures and. First Geological structures will be detected from the primary photographic dataset and the equivalent three dimensional structures would then be identified by digital elevation model. We can calculate dip and its direction by using the above information. The structural map will be generated by adopting a specified methodology starting from choosing the appropriate camera, camera’s mounting system, UAVs design ( based on the area and application), Challenge in air borne systems like Errors in image orientation, payload problem, mosaicing and geo referencing and registering of different images to applying DEM. The paper shows the potential of using our method for accurate and efficient modeling of geological structures, capture particularly from remote, of inaccessible and hazardous sites.

Keywords: digital elevation model, mapping, photogrammetric data analysis, geological structures

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Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

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Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

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Authors: Shih-Ching Wang, Mark Lang

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This paper examines the secondary effects and consequences of special displays on subsequent shopping behavior. Special displays are studied as a prominent form of in-store or shopper marketing activity. Two experiments are performed using special value and special quality-oriented displays in an online simulated store environment. The impact of exposure to special displays on mindsets and resulting product choices are tested in a shopping task. Impact on store image is also tested. The experiments find that special displays do trigger shopping mindsets that affect product choices and shopping basket composition and value. There are intended and unintended positive and negative effects found. Special value displays improve store price image but trigger a price sensitive shopping mindset that causes more lower-priced items to be purchased, lowering total basket dollar value. Special natural food displays improve store quality image and trigger a quality-oriented mindset that causes fewer lower-priced items to be purchased, increasing total basket dollar value. These findings extend the theories of product categorization, mind-sets, and price sensitivity found in communication research into the retail store environment. Findings also warn retailers to consider the total effects and consequences of special displays when designing and executing in-store or shopper marketing activity.

Keywords: special displays, mindset, shopping behavior, price consciousness, product categorization, store image

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Authors: Tatsuya Takino, Izuru Nomura, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of detecting the craters from the image of the lunar surface. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) working group aiming at the pinpoint landing on the lunar surface and investigating scientific research. It is difficult to equip and use high-performance computers for the small space probe. So, it is necessary to use a small computer with an exclusive hardware such as FPGA. We have studied the crater detection using principal component analysis (PCA), In this paper, We implement detection algorithm into the FPGA, and the detection is performed on the data that was captured from the CMOS camera.

Keywords: crater detection, PCA, FPGA, image processing

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Authors: Cristina Azocar, Ivana Markova

Abstract:

A survey of 565 male undergraduates examined the effects of exposure to social networking sites and entertainment media on young men’s body image. Exposure to social and to entertainment media was found to have negative effects on men’s body satisfaction, social comparison, and thin ideal internalization. Findings indicated significant differences in those men who were more exposed to social and to entertainment media than those who were not as exposed. Consistent with past studies, gay men were found to be more dissatisfied with their bodies than straight men. Gay men compared themselves to other better-looking individuals and internalized ideal body types seen in media significantly more than their straight counterparts. Surprisingly, straight men seem to care as much about their physical attractiveness/appearance as gay men do, but only in public settings such as at the beach, at athletic events (including gyms) and social events. Although on average ethnic groups were more similar than different, small but significant differences occurred with Asian men indicating significantly higher body dissatisfaction than White/European men and Middle Eastern/Arab men their counterparts. The study increases our knowledge about SNS and entertainment use and its associated body image, and body satisfaction affects among low-income ethnic minority men.

Keywords: body dissatisfaction, body image, entertainment media, gay men, race and ethnicity, social economic status, social comparison, social media

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

Abstract:

Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Ugo Rovigatti

Abstract:

Neuroblastoma (NBL) has epitomized for at least 40 years our understanding of cancer cellular and molecular biology and its potential applications to novel therapeutic strategies. This includes the discovery of the very first oncogene aberrations and tumorigenesis suppression by differentiation in the 80s; the potential role of suppressor genes in the 90s; the relevance of immunotherapy in the millennium first, and the discovery of additional mutations by NGS technology in the millennium second decade. Similar discoveries were achieved in the majority of human cancers, and similar therapeutic interventions were obtained subsequently to NBL discoveries. Unfortunately, targeted therapies suggested by specific mutations (such as MYCN amplification –MNA- present in ¼ or 1/5 of cases) have not elicited therapeutic successes in aggressive NBL, where the prognosis is still dismal. The reasons appear to be linked to Tumor Heterogeneity, which is particularly evident in NBL but also a clear hallmark of aggressive human cancers generally. The new avenue of cancer immunotherapy (CIT) provided new hopes for cancer patients, but we still ignore the cellular or molecular targets. CIT is emblematic of high-risk disease (HR-NBL) since the mentioned GD2 passive immunotherapy is still providing better survival. We recently critically reviewed and evaluated the literature depicting the genomic landscapes of HR-NBL, coming to the qualified conclusion that among hundreds of affected genes, potential targets, or chromosomal sites, none correlated with anti-GD2 sensitivity. A better explanation is provided by the Micro-Foci inducing Virus (MFV) model, which predicts that neuroblasts infection with the MFV, an RNA virus isolated from a cancer-cluster (space-time association) of HR-NBL cases, elicits the appearance of MNA and additional genomic aberrations with mechanisms resembling chromothripsis. Neuroblasts infected with low titers of MFV amplified MYCN up to 100 folds and became highly transformed and malignant, thus causing neuroblastoma in young rat pups of strains SD and Fisher-344 and larger tumor masses in nu/nu mice. An association was discovered with GD2 since this glycosphingolipid is also the receptor for the family of MFV virus (dsRNA viruses). It is concluded that a dsRNA virus, MFV, appears to provide better explicatory mechanisms for the genesis of i) specific genomic aberrations such as MNA; ii) extensive tumor heterogeneity and chromothripsis; iii) the effects of passive immunotherapy with anti-GD2 monoclonals and that this and similar models should be further investigated in both pediatric and adult cancers.

Keywords: neuroblastoma, MYCN, amplification, viruses, GD2

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Authors: Brajesh Tiwari, Yuvraj Dangi, Abhishek Jain, Ashok Jain

Abstract:

The purpose of the investigation was to evaluate the potential of sphingosomes as nanoscale drug delivery units for site-specific delivery of anti-cancer agents. Doxorubicin Hydrochloride (DOX) was selected as a model anti-cancer agent. Sphingosomes were prepared and loaded with DOX and optimized for size and drug loading. The formulations were characterized by Malvern zeta-seizer and Transmission Electron Microscopy (TEM) studies. Sphingosomal formulations were further evaluated for in-vitro drug release study under various pH profiles. The in-vitro drug release study showed an initial rapid release of the drug followed by a slow controlled release. In vivo studies of optimized formulations and free drug were performed on albino rats for comparison of drug plasma concentration. The in- vivo study revealed that the prepared system enabled DOX to have had enhanced circulation time, longer half-life and lower elimination rate kinetics as compared to free drug. Further, it can be interpreted that the formulation would selectively enter highly porous mass of tumor cells and at the same time spare normal tissues. To summarize, the use of sphingosomes as carriers of anti-cancer drugs may prove to be a fascinating approach that would selectively localize in the tumor mass, increasing the therapeutic margin of safety while reducing the side effects associated with anti-cancer agents.

Keywords: sphingosomes, anti-cancer, doxorubicin, formulation

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Authors: Nathenal Thomas

Abstract:

In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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