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The bone age of a child indicates the skeletal and biological maturity of an individual. The most commonly applied clinical methods for Bone Age Assessment (BAA) are based on the visual examination of ossification of individual bones in radiographs of the left hand and the wrist by comparing with standard hand atlas. This kind of method is highly subjective and the performance extremely depends on...
A prime factor deciding the survival rate of a breast cancer patient is the accuracy with which the malignancy grade of a breast tumor is determined. A Fine Needle Aspiration (FNA) biopsy is a key mechanism for breast cancer diagnosis as well as for assigning grades to malignant cases. In this paper, based on published cytological malignancy grading systems, we propose six computer-aided grading frameworks...
Iris recognition for some time now has been a challenging exercise. This perhaps is due to the use of inappropriate descriptors during the feature extraction stage. In this paper, a Radon Transform is used as an iris signature descriptor. Blood vessels are segmented from iris image. After blood vessel segmentation, the radon transform is applied on the segmented image. The GLCM, Gabor and Local Binary...
Aortic stenosis (AS) is a condition where the calcification deposit within the heart leaflets narrows the valve and restricts the blood from flowing through it. This disease is progressive over time where it may affect the mechanism of the heart valve. To alleviate this condition without resorting to surgery, which runs the risk of mortality, a new method of treatment has been introduced: Transcatheter...
This paper explores the feasibility of using multiframe analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Baretts Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers...
Diabetes is a disease that reduces the human body's ability to store and regulate sugar. This disease develops due to excessive intake of food with higher sugar, excessive work pressure or unbalanced routines lacking in proper diet. Diabetes once developed is then harder to overcome and thus effects the human body functioning leading to failure of many human body parts. One of the major problem associated...
Since red lesions have been found to be one of the earliest lesions in diabetic retinopathy (DR), automatic red lesions detection plays a critical role in diabetic retinopathy diagnosis. In this article, we develop a novel method using superpixel segmentation and multi-feature classification (SMFC). Using our proposed method, the retinal images are segmented into superpixels with the similar color...
Interstitial fibrosis in renal biopsies has shown a good correlation to the presence of chronic kidney disease, and it is therefore quantified by pathologists in the diagnosis of the disease. In the previous work, the developed automatic quantification system for the interstitial fibrosis was presented. It was based on the segmentation of tubular structures. This paper advances the development of...
Diabetic retinopathy(DR) is one of the blinding complication of diabetes mellitus. In diabetic patients, the regular retinal exams are essential. There are various therapies like intravitreal medical therapy and sutureless pars-plana vitrectomy that have improved ophthalmic care of patients suffering from diabetes. Diagnosis and treatment of several disorders that affects the retina and the choroid...
This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders...
Computerized prenatal ultrasound (US) image segmentation methods can greatly improve the efficiency and objectiveness of the biometry interpretation. However, the boundary incompleteness and ambiguity in US images hinder the automatic solutions severely. In this paper, we propose a cascaded framework for fully automatic US image segmentation. A customized Fully Convolutional Network (FCN) was utilized...
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages...
In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning...
Ultrasound Imaging is one of the most widely used technique to provide information about renal diseases in kidney such as cyst, tumor and calculi. This paper aims to extract features from the different renal abnormalities to discriminate between the normal and abnormal conditions. Two filters, median and wiener filter are used to remove the speckle noise in US (ultrasound) images. A picture quality...
This work proposes an efficient clustering technique for the localization of normal and abnormal tissues using the thermal data obtained from Digital Infrared Thermal Imaging. 10 normal and abnormal raw thermograms are preprocessed and by using K-means clustering, the heat patterns of the thermograms are clustered into various objects using the Euclidean distance metric. Further, breast thermograms...
There are countless plant species available globally. To manage massive content, development of a fast and effective categorization methods has turned into a territory of dynamic research. As trees and plants are very important to ecology, accurate Identification and classification becomes necessary. Classification procedure is carried out through number of sub procedures. An identification or Classification...
Detection and counting of white blood cells (WBC) in blood samples provides valuable information to medical specialists, helping them to evaluate a wide range of important hematic pathologies such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and time consuming. An automatic detection and classification of WBC images can enhance the accuracy and speed up the detection...
Vascular networks in infrared faces are created due to the blood flow under the skin. Variations in blood flow in the blood vessels cause temperature difference, which produces the vascular networks. This paper deals with binary classification of various infrared facial expressions using vascular network. The classification has been performed using Support Vector Machine classifier on five types of...
This paper proposes a novel and simple unsupervised vessel segmentation algorithm using fundus images. At first, the green channel of a fundus image is preprocessed to extract a binary image after the isotropic undecimated wavelet transform, and another binary image from the morphologically reconstructed image. Secondly, two initial vessel images are extracted according to the vessel region features...
Automatic segmentation of the normal features such the blood vessels may help to develop the future of medicine. It can give an earlier diagnosis of such eye diseases as diabetic retinopathy and glaucoma; it can support the specialists in their decision as well. In this paper, a method of retinal blood vessel segmentation is proposed. The database used is HRF of a total of 45 fundus images. The fundus...
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