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The paper proposes a dynamic thresholding based image processing technique for the detection of hemorrhages in retinal images. The algorithm uses the information about color and size of hemorrhages as a tool for classifying hemorrhages from other dark lesions present in the retinal images. The algorithm uses the concepts of contrast enhancement, background estimation and intensity variation at edges...
Reliable, fast and efficient optic disc localization and blood-vessel detection are the primary tasks in computer analyses of retinal image. Most of the existing algorithms suffer due to inconsistent image contrast, varying individual condition, noises and computational complexity. This paper presents an algorithm to automatically detect landmark features of retinal image, such as optic disc and blood...
We present a Sparse Representation-based Classifier (SRC) that provides superior performance in terms of high Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) in classifying benign and malignant breast lesions captured in ultrasound images. Although such a classifier was proposed for face recognition, it has been proposed in medical diagnosis from ultrasonic images in this work for...
Microstimulation is a feasible method targeting visual impairment. In this paper, the authors focus on intra-cortical stimulation to cover the broader spectrum of the issue with the aim of providing a better suited visual aid. They present an overall modular architecture and focus on creating re-usable image processing tools that can be used for image simplification and recognition tasks. One of the...
Image Segmentation is an important part of image processing. It is used in medical field to detect and to diagnose the death threatening diseases. Manual readings can be done to analyze the medical images. But still the result leads to misdiagnosis by manual segmentation and the accuracy is not so high. Many Computer Aided Detection systems arise to increase the accuracy and performance rate. In the...
In this paper, we present a new classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from...
Cytogenetic is a branch of genetics that is concerned with the study of the structure and function of the cell, especially the chromosomes. The chromosomal identification is of prime importance to geneticist for diagnosing various abnormalities. The existing system is developed to classify the chromosomes based on pixel distribution, centromere index and band patterns using artificial neural network...
Automated MRI segmentation techniques are helpful for a physician for early diagnosis of degenerating diseases in individual patients. Here we are using the T1weighted axial MR images of neuro degenerative diseases. The assessment of the accuracy of the result is done by an expert. FCM an unsupervised clustering technique is implemented in order to classify the brain voxel. The brain voxels are classified...
Recently, the progresses of human-computer interface technology implemented on tablet PCs (personal computers) enables medical workers to utilize them for communications between patients and doctors at the time of condition analysis and diagnosis. Because general patients cannot understand medical images alone, the technology assists them to intuitively understand the images and easily communicate...
Brain hemorrhage is a type of stroke which is caused by an artery in the brain bursting and causing bleeding in the surrounded tissues. Diagnosing brain hemorrhage, which is mainly through the examination of a CT scan enables the accurate prediction of disease and the extraction of reliable and robust measurement for patients in order to describe the morphological changes in the brain as the recovery...
Medulloblastoma (MB) is the most common brain tumor in children. There are four distinct subtypes of MB, but patients with anaplastic/large cell have the worst prognosis. Since the morbidity is highly correlated with treatment for MB, the ability to distinguish aggressive (such as anaplastic/large cell) MB is crucial. We present a scheme that leverages quantitative image texture features (Haar, Haralick,...
Recently, accidents such that seniors fall down from the bed in care facilities or hospitals are increased. To prevent these accidents, we have developed the awakening behavior detection system using Neural Network. In this paper, it is a problem that the detection success rate of the current system using captured image in the clinical site is not enough. So, we analyze the captured image in the clinical...
The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research...
With the sophistication in automated computing systems Bio-Medical Image analysis is made simple. Today there is an increase in interest for setting up medical system that can screen a large number of people for sight threatening diseases, such Retinoblastoma (Rb) and Diabetic Retinopathy(DR). Spatial Domain Edge Detection approach needs Gray scale images for feature extraction and highly prone to...
Mammography is a well established imaging technique for showing tissue abnormalities of breast and has been proven to reduce death rate due to breast cancer in screened populations of women. The proposed method classifies the breast tissues according to severeness of abnormality (benign or malign) using combined transform domain features. The discrete wavelet transform (DWT) features are merged with...
Melanoma can be cured if it is detected early, so early diagnosis is very important in dermatological practice today. Early and non-invasive diagnosis of melanomas can be done by accurate image segmentation of skin lesions. The medical images, while acquisition are generally bound to contain noise. This paper proposes a robust and efficient image segmentation algorithm using LOG edge detector to extract...
The main goal of this paper is to show the prominent applications of the wavelet and multiresolution transform for cancer diagnosis system. Early detection and diagnosis of breast cancer helps the survival rate of the patient. Wavelet transform and the multiresolution analysis of the image processing techniques contribute to the vital role in the medical applications. Some of the image processing...
According to the statistics, melanoma accounts for just 11 % of all types of skin cancer, it is responsible for most of the deaths. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of image processing techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as...
Breast Cancer is one of the frequent and leading causes of mortality among woman, especially in developed countries. Woman within the age of 40-69 have more risk of breast cancer. Though breast cancer leads to death, early detection of breast cancer can increase the survival rate. Clustered Microcalcification (MC) in mammograms is the major indication for early detection of breast cancer. MC is quiet...
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