Search results for: medical image processing

Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of liver and spleen from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of ROI from CT images is usually a difficult task. This difficulty is the gray’s level of which is similar to the other organ also the ROI are connected to the ribs, heart, kidneys, etc. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the surrounding and connected organs and tissues by applying morphological filters. This first step makes the extraction of interest regions easier. The second step consists of improving the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce these deficiencies by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: S. Khan, A. Khan, Abdul R. Soomrani, Raja F. Zafar, A. Waqas, G. Akbar

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This paper proposes a low-complexity image interpolation method. Image interpolation is used to convert a low dimension video/image to high dimension video/image. The objective of a good interpolation method is to upscale an image in such a way that it provides better edge preservation at the cost of very low complexity so that real-time processing of video frames can be made possible. However, low complexity methods tend to provide real-time interpolation at the cost of blurring, jagging and other artifacts due to errors in slope calculation. Non-linear methods, on the other hand, provide better edge preservation, but at the cost of high complexity and hence they can be considered very far from having real-time interpolation. The proposed method is a linear method that uses gradient orientation for slope calculation, unlike conventional linear methods that uses the contrast of nearby pixels. Prewitt edge detection is applied to separate uniform regions and edges. Simple line averaging is applied to unknown uniform regions, whereas unknown edge pixels are interpolated after calculation of slopes using gradient orientations of neighboring known edge pixels. As a post-processing step, bilateral filter is applied to interpolated edge regions in order to enhance the interpolated edges.

Keywords: edge detection, gradient orientation, image upscaling, linear interpolation, slope tracing

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Amir Moslemi, Amir movafeghi, Shahab Moradi

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One of the most important challenging factors in medical images is nominated as noise.Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjected to low quality due to the noise. The quality of CT images is dependent on the absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on the purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete wavelet transform(DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result in good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim).

Keywords: computed tomography (CT), noise reduction, curve-let, contour-let, signal to noise peak-peak ratio (PSNR), structure similarity (Ssim), absorbed dose to patient (ADP)

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Authors: Bülent Kantar, Numan Ünaldı

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This paper presents a new method which includes robust and invisible digital watermarking on images that is colored. Colored images are used as watermark. Frequency region is used for digital watermarking. Discrete wavelet transform and discrete stationary wavelet transform are used for frequency region transformation. Low, medium and high frequency coefficients are obtained by applying the two-level discrete wavelet transform to the original image. Low frequency coefficients are obtained by applying one level discrete stationary wavelet transform separately to all frequency coefficient of the two-level discrete wavelet transformation of the original image. For every low frequency coefficient obtained from one level discrete stationary wavelet transformation, watermarks are added. Watermarks are added to all frequency coefficients of two-level discrete wavelet transform. Totally, four watermarks are added to original image. In order to get back the watermark, the original and watermarked images are applied with two-level discrete wavelet transform and one level discrete stationary wavelet transform. The watermark is obtained from difference of the discrete stationary wavelet transform of the low frequency coefficients. A total of four watermarks are obtained from all frequency of two-level discrete wavelet transform. Obtained watermark results are compared with real watermark results, and a similarity result is obtained. A watermark is obtained from the highest similarity values. Proposed methods of watermarking are tested against attacks of the geometric and image processing. The results show that proposed watermarking method is robust and invisible. All features of frequencies of two level discrete wavelet transform watermarking are combined to get back the watermark from the watermarked image. Watermarks have been added to the image by converting the binary image. These operations provide us with better results in getting back the watermark from watermarked image by attacking of the geometric and image processing.

Keywords: watermarking, DWT, DSWT, copy right protection, RGB

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Razan Farah, Siham Ballah

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Facebook, Instagram, TikTok and other social media applications have become an integral component of everyone’s social life, particularly among younger generations and adolescences. These social apps have been changing a lot of conceptions and believes in the population by representing public figures and celebrities as role models. The social comparison theory, which says that people self-evaluate based on comparisons with similar others, is commonly used to explore the impact of social media on body image. There is a need to study the influence of those social platforms on the body image as there have been an increase in body dissatisfaction in the recent years. This cross sectional study used a self administered questionnaire on a simple random sample of 133 female medical students of the first year. Finding shows that the response rate was 75%. There was an association between social media usage and noticing how the person look(p value = .022), but no significant association between social media use and body image influence or dissatisfaction was found. This study implies more research under this topic in Sudan as the literature are scarce.

Keywords: body image, body dissatisfaction, social media, adolescences

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Authors: Mrinal Kanti Bhowmik, Shawli Bardhan Jr., Debotosh Bhattacharjee

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Human skin containing temperature value more than absolute zero, discharges infrared radiation related to the frequency of the body temperature. The difference in infrared radiation from the skin surface reflects the abnormality present in human body. Considering the difference, detection and forecasting the temperature variation of the skin surface is the main objective of using Medical Infrared Thermography(MIT) as a diagnostic tool for pain detection. Medical Infrared Thermography(MIT) is a non-invasive imaging technique that records and monitors the temperature flow in the body by receiving the infrared radiated from the skin and represent it through thermogram. The intensity of the thermogram measures the inflammation from the skin surface related to pain in human body. Analysis of thermograms provides automated anomaly detection associated with suspicious pain regions by following several image processing steps. The paper represents a rigorous study based survey related to the processing and analysis of thermograms based on the previous works published in the area of infrared thermal imaging for detecting inflammatory pain diseases like arthritis, spondylosis, shoulder impingement, etc. The study also explores the performance analysis of thermogram processing accompanied by thermogram acquisition protocols, thermography camera specification and the types of pain detected by thermography in summarized tabular format. The tabular format provides a clear structural vision of the past works. The major contribution of the paper introduces a new thermogram acquisition standard associated with inflammatory pain detection in human body to enhance the performance rate. The FLIR T650sc infrared camera with high sensitivity and resolution is adopted to increase the accuracy of thermogram acquisition and analysis. The survey of previous research work highlights that intensity distribution based comparison of comparable and symmetric region of interest and their statistical analysis assigns adequate result in case of identifying and detecting physiological disorder related to inflammatory diseases.

Keywords: acquisition protocol, inflammatory pain detection, medical infrared thermography (MIT), statistical analysis

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Authors: Auday Al-Mayyahi, Phil Birch, William Wang

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A vision system-based navigation for autonomous ground vehicle (AGV) equipped with a single camera in an indoor environment is presented. A proposed navigation algorithm has been utilized to detect obstacles represented by coloured mini- cones placed in different positions inside a corridor. For the recognition of the relative position and orientation of the AGV to the coloured mini cones, the features of the corridor structure are extracted using a single camera vision system. The relative position, the offset distance and steering angle of the AGV from the coloured mini-cones are derived from the simple corridor geometry to obtain a mapped environment in real world coordinates. The corridor is first captured as an image using the single camera. Hence, image processing functions are then performed to identify the existence of the cones within the environment. Using a bounding box surrounding each cone allows to identify the locations of cones in a pixel coordinate system. Thus, by matching the mapped and pixel coordinates using a projection transformation matrix, the real offset distances between the camera and obstacles are obtained. Real time experiments in an indoor environment are carried out with a wheeled AGV in order to demonstrate the validity and the effectiveness of the proposed algorithm.

Keywords: autonomous ground vehicle, navigation, obstacle avoidance, vision system, single camera, image processing, ultrasonic sensor

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Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi, and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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Authors: Ahmad Absetan, Mahdi Nooshyar

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The importance map is an image-based measure and is a core part of the resizing algorithm. Importance measures include image gradients, saliency and entropy, as well as high level cues such as face detectors, motion detectors and more. In this work we proposed a new method to calculate the importance map, the importance map is generated automatically using a novel combination of image edge density and Harel saliency measurement. Experiments of different type images demonstrate that our method effectively detects prominent areas can be used in image resizing applications to aware important areas while preserving image quality.

Keywords: content-aware image resizing, visual saliency, edge density, image warping

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Authors: Singara Singh Kasana, Pankaj Garg

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In this paper, a blind data hiding technique based on interpolation of sub sampled versions of a cover image is proposed. Sub sampled image is taken as a reference image and an interpolated image is generated from this reference image. Then difference between original cover image and interpolated image is used to embed secret data. Comparisons with the existing interpolation based techniques show that proposed technique provides higher embedding capacity and better visual quality marked images. Moreover, the performance of the proposed technique is more stable for different images.

Keywords: interpolation, image subsampling, PSNR, SIM

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Authors: Leo Carlo B. Rondina

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Self-image is an important factor to improve the self-esteem of the personnel. The purpose of the study is to determine the self-image of the police. The respondents were the 503 policemen assigned in different Police Station in Davao City, and they were chosen with the used of random sampling. With the used of Exploratory Factor Analysis (EFA), latent construct variables of police image were identified as follows; professionalism, obedience, morality and justice and fairness. Further, ordinal regression indicates statistical characteristics on ages 21-40 which means the age of the respondent statistically improves self-image.

Keywords: police image, exploratory factor analysis, ordinal regression, Galatea effect

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Authors: Guofan Shao, Lina Tang, Hao Zhang

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The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified.

Keywords: accuracy assessment, efficacy, image classification, machine learning, uncertainty

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Authors: Mahdi Motahari, Mojtaba Farzaneh, Ebrahim Sepidbar

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Tracking of moving and flying targets is one of the most important issues in image processing topic. Estimating of trajectory of desired object in short-term and long-term scale is more important than tracking of moving and flying targets. In this paper, a new way of identifying and estimating of future trajectory of a moving ball in long-term scale is estimated by using synthesis and interaction of image processing algorithms including noise removal and image segmentation, Kalman filter algorithm in order to estimating of trajectory of ball in football game in short-term scale and intelligent adaptive neuro-fuzzy algorithm based on time series of traverse distance. The proposed system attain more than 96% identify accuracy by using aforesaid methods and relaying on aforesaid algorithms and data base video in format of synthesis and interaction. Although the present method has high precision, it is time consuming. By comparing this method with other methods we realize the accuracy and efficiency of that.

Keywords: tracking, signal processing, moving targets and flying, artificial intelligent systems, estimating of trajectory, Kalman filter

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Authors: Yogesh Karunakar, Praveen Kandaswamy

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Mother’s milk provides the most superior source of nutrition to a child. There is no other substitute to the mother’s milk. Postpartum secretions like breast milk can be analyzed on the go for testing the presence of any harmful pathogen before a mother can feed the child or donate the milk for the milk bank. Since breast feeding is one of the main causes for transmission of diseases to the newborn, it is mandatory to test the secretions. In this paper, we describe the detection of pathogens like E-coli, Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), Hepatitis C (HCV), Cytomegalovirus (CMV), Zika and Ebola virus through an innovative method, in which we are developing a unique chip for testing the mother’s milk sample. The chip will contain an antibody specific to the target pathogen that will show a color change if there are enough pathogens present in the fluid that will be considered dangerous. A smart-phone camera will then be acquiring the image of the strip and using various image processing techniques we will detect the color development due to antigen antibody interaction within 5 minutes, thereby not adding to any delay, before the newborn is fed or prior to the collection of the milk for the milk bank. If the target pathogen comes positive through this method, then the health care provider can provide adequate treatment to bring down the number of pathogens. This will reduce the postpartum related mortality and morbidity which arises due to feeding infectious breast milk to own child.

Keywords: postpartum, fluids, camera, HIV, HCV, CMV, Zika, Ebola, smart-phones, breast milk, pathogens, image processing techniques

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Authors: Amhimmid Mohammed Saffour, Abdelkader Salama

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Filtering is a part of image enhancement techniques, it is used to enhance certain details such as edges in the image that are relevant to the application. Additionally, filtering can even be used to eliminate unwanted components of noise. Medical images typically contain salt and pepper noise and Poisson noise. This noise appears to the presence of minute grey scale variations within the image. In this paper, different filters techniques namely (Median, Wiener, Rank order3, Rank order5, and Average) were applied on CT medical images (Brain and chest). We using all these filters to remove salt and pepper noise from these images. This type of noise consists of random pixels being set to black or white. Peak Signal to Noise Ratio (PSNR), Mean Square Error r(MSE) and Histogram were used to evaluated the quality of filtered images. The results, which we have achieved shows that, these filters, are more useful and they prove to be helpful for general medical practitioners to analyze the symptoms of the patients with no difficulty.

Keywords: CT imaging, median filter, adaptive filter and average filter, MATLAB

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Authors: Yi-Shiuan Chiu

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Deaf signers have been found to have better mental image performance than hearing nonsigners. The goal of this study was to investigate the ability to generate mental images, to maintain them, and to manipulate them in deaf signers of Taiwanese Sign Language (TSL). In the visual image task, participants first memorized digits formed in a cell of 4 × 5 grids. After presenting a cue of Chinese digit character shown on the top of a blank cell, participants had to form a corresponding digit. When showing a probe, which was a grid containing a red circle, participants had to decide as quickly as possible whether the probe would have been covered by the mental image of the digit. The ISI (interstimulus interval) between cue and probe was manipulated. In experiment 1, 24 deaf signers and 24 hearing nonsigners were asked to perform image generation tasks (ISI: 200, 400 ms) and image maintenance tasks (ISI: 800, 2000 ms). The results showed that deaf signers had had an enhanced ability to generate and maintain a mental image. To explore the process of mental image, in experiment 2, 30 deaf signers and 30 hearing nonsigners were asked to do visual searching when maintaining a mental image. Between a digit image cue and a red circle probe, participants were asked to search a visual search task to see if a target triangle apex was directed to the right or left. When there was only one triangle in the searching task, the results showed that both deaf signers and hearing non-signers had similar visual searching performance in which the searching targets in the mental image locations got facilitates. However, deaf signers could maintain better and faster mental image performance than nonsigners. In experiment 3, we increased the number of triangles to 4 to raise the difficulty of the visual search task. The results showed that deaf participants performed more accurately in visual search and image maintenance tasks. The results suggested that people may use eye movements as a mnemonic strategy to maintain the mental image. And deaf signers had enhanced abilities to resist the interference of eye movements in the situation of fewer distractors. In sum, these findings suggested that deaf signers had enhanced mental image processing.

Keywords: deaf signers, image maintain, mental image, visual search

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Authors: Vinay P., Srinivas K. S.

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Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown.

Keywords: low light image enhancement, real-time video, computer vision, machine learning

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Authors: Ibtihal D. Mustafa, Mawia A. Hassan

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Tumor segmentation from MRI image is important part of medical images experts. This is particularly a challenging task because of the high assorting appearance of tumor tissue among different patients. MRI images are advance of medical imaging because it is give richer information about human soft tissue. There are different segmentation techniques to detect MRI brain tumor. In this paper, different procedure segmentation methods are used to segment brain tumors and compare the result of segmentations by using correlation and structural similarity index (SSIM) to analysis and see the best technique that could be applied to MRI image.

Keywords: MRI, segmentation, correlation, structural similarity

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm

Keywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm

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Authors: S. Sushma, S. Balasubramanian, K. C. Latha

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The mammographic image of breast cancer compressed and synthesized to get co-efficient values which will be converted (5x5) matrix to get ROI image where we get the highest value of effected region and with the same ideology the technique has been extended to differentiate between Calcification and normal cell image using mean value derived from 5x5 matrix values

Keywords: texture analysis, mammographic image, partitioned gray scale co-oocurance matrix, co-efficient

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Authors: Aarushi Agarwal, Tara Singh, I.L Singh, Anju Lata Singh, Trayambak Tiwari

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Simple discrimination in near absence of attention has been widely observed. Dual-task studies with natural scenes studies have been claimed as being preattentive in nature that facilitated categorization simultaneously with the attentional demanding task. So in this study, multiple images at the periphery are presented, initiating parallel processing in near absence of attention. For the central demanding task rotated letters were presented in both conditions, while in periphery natural and animal images were presented. To understand the breakpoint of ability to perform in near absence of attention one, two and three peripheral images were presented simultaneously with central task and subjects had to respond when all belong to the same category. Individual participant performance did not show a significant difference in both conditions central and peripheral task when the single peripheral image was shown. In case of two images high-level parallel processing could take place with little attentional resources. The eye tracking results supports the evidence as no major saccade was made in a large number of trials. Three image presentations proved to be a breaking point of the capacities to perform outside attentional assistance as participants showed a confused eye gaze pattern which failed to make the natural and animal image discriminations. Thus, we can conclude attention and awareness being independent mechanisms having limited capacities.

Keywords: attention, dual task pardigm, parallel processing, break point, saccade

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Authors: Dahi Ghareab Abdelsalam Ibrahim, R. H. Bakr

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Ionizing radiation, such as gamma radiation and X-rays, has many applications in medical diagnoses and cancer treatment. In this paper, we used the windowed Fourier transform to extract the complex image of the deformed red blood cells. The real values of the complex image are used to extract the best fitting of the deformed cell boundary. Male albino rats are irradiated by γ-rays from ⁶⁰Co. The male albino rats are anesthetized with ether, and then blood samples are collected from the eye vein by heparinized capillary tubes for studying the radiation-damaging effect in-vivo by the proposed windowed Fourier transform. The peripheral blood films are prepared according to the Brown method. The peripheral blood film is photographed by using an Automatic Image Contour Analysis system (SAMICA) from ELBEK-Bildanalyse GmbH, Siegen, Germany. The SAMICA system is provided with an electronic camera connected to a computer through a built-in interface card, and the image can be magnified up to 1200 times and displayed by the computer. The images of the peripheral blood films are then analyzed by the windowed Fourier transform method to extract the precise deformation from the best fitting. Based on accurate deformation evaluation of the red blood cells, diseases can be diagnosed in their primary stages.

Keywords: windowed Fourier transform, red blood cells, phase wrapping, Image processing

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

Procedia PDF Downloads 78

Authors: Kldiashvili Ekaterina, Burduli Archil, Ghortlishvili Gocha

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Introduction – Medical information system (MIS) is at the heart of information technology (IT) implementation policies in healthcare systems around the world. Different architecture and application models of MIS are developed. Despite of obvious advantages and benefits, application of MIS in everyday practice is slow. Objective - On the background of analysis of the existing models of MIS in Georgia has been created a multi-user web-based approach. This presentation will present the architecture of the system and its application for image based second opinion consultations. Methods – The MIS has been created with .Net technology and SQL database architecture. It realizes local (intranet) and remote (internet) access to the system and management of databases. The MIS is fully operational approach, which is successfully used for medical data registration and management as well as for creation, editing and maintenance of the electronic medical records (EMR). Five hundred Georgian language electronic medical records from the cervical screening activity illustrated by images were selected for second opinion consultations. Results – The primary goal of the MIS is patient management. However, the system can be successfully applied for image based second opinion consultations. Discussion – The ideal of healthcare in the information age must be to create a situation where healthcare professionals spend more time creating knowledge from medical information and less time managing medical information. The application of easily available and adaptable technology and improvement of the infrastructure conditions is the basis for eHealth applications. Conclusion - The MIS is perspective and actual technology solution. It can be successfully and effectively used for image based second opinion consultations.

Keywords: digital images, medical information system, second opinion consultations, electronic medical record

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

Abstract:

Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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