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This paper presents a computational based system for detection and classification of lung nodules from chest CT scan images. In this study we consider the case of a primary lung cancer. Optimal thresholding and gray level characteristics are used for segmentation of lung nodules from the lung volume area. After detection of lung mass tissue, geometrical features are extracted. Simple image processing...
Blood vessel extraction from retinal fundus images is an important task in developing the computer-aided diagnostic system for ophthalmologists. In this paper we have presented an algorithm for extraction of blood vessels of retinal fundus images and comparison of different moment invariants used for the extraction of features for the vessel pixels. The algorithm uses neural networks for distinguishing...
Malaria remains a public health problem in Indonesia. There are still many deaths caused by malaria, particularly in eastern Indonesia. There are two types of blood perform in malaria, thick blood film and thin blood film. In Indonesia, thin blood film is used more frequently than thick blood film. Malaria parasites can be found in thick blood film rapidly due to the higher volume of the blood used...
A modified Gram-Schmidt orthogonalisation method has been commixed with a region growing method for efficient white blood cell image segmentation. The modified Gram-Schmidt method is used to segment the nucleus of white blood cells, while the region growing method is employed to segment the cytoplasm of white blood cells. To evaluate the performance of WBC image segmentation, the 100 samples of the...
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes...
Radiologists are known to suffer from fatigue and drop in diagnostic accuracy due to large number of slices to read and long working hours. A computer aided diagnosis (CAD) system could help lighten the workload. Segmentation is the first step in a CAD system. This study aims to propose an accurate automatic segmentation. This study deals with High Resolution Computed Tomography (HRCT) scans of the...
Autoimmune diseases occur when an inappropriate immune response takes place and produces autoantibodies to fight against human antigens. In order to detect autoimmune disease, a test, called indirect immunofluorescence (IIF) is carried out to identify antinuclear autoantibodies (ANA) in the HEp-2 cell. Current method of analyzing the results is inconsistent as it is limited to subjective factors such...
Wheat diseases are harmful to wheat production, but there are few segmentation algorithms that can effectively identify common diseases of wheat leaves. This paper proposes an automatic and efficient solution with K-means clustering. Firstly, the colour image is transformed to Lab colour space from RGB. Clustering is then done by taking the absolute difference between each pixel and the clustering...
Plant disease management is an important factor in agriculture as it causes a significant yield loss in crops. Late Blight is the most devastating disease for Potato in most of the potato growing regions in the world. For optimum use of pesticide and to minimize the yield loss, the identification of disease severity is essential. The key contribution here is an algorithm to determine the severity...
We propose a active learning (AL) approach to segment Crohn's disease (CD) affected regions in abdominal magnetic resonance (MR) images. Our label query strategy is inspired from the principles of visual saliency which has similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low level features. The most informative node...
Plant disease analysis is one of the critical tasks in the field of agriculture. Automatic identification and classification of plant diseases can be supportive to agriculture yield maximization. In this paper we compare performance of several Machine Learning techniques for identifying and classifying plant disease patterns from leaf images. A three-phase framework has been implemented for this purpose...
Chronic lymphocytic leukemia (CLL) is the most common type of blood cancer in Canadian adults. CLL cells are abnormal lymphocytes, which tend to be slightly larger than normal resting lymphocytes and have a condensed appearance to their chromatin. There is a low number of related works on this disease. This paper presents a method to segment normal and CLL lymphocytes into two parts: nucleus, and...
We address the problem of weakly supervised segmentation (WSS) of medical images which is more challenging and has potentially greater applications in the medical imaging community. Training images are labeled only by the classes they contain, and not by the pixel labels. We make use of the Multi Image Model (MIM) for weakly supervised segmentation which exploits superpixel features and assigns labels...
Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps, in the first step K-Means clustering technique is used for the image segmentation,...
This paper focuses on the medical image segmentation techniques used in the study of spinal diseases. Medical reports have shown an increasing concern of the public on spinal diseases caused by disc degeneration. This paper presents an accurate and automated method to detect the abnormal disc. This method uses two standard models in conjunction with the threshold value to accurately identify the cartilage...
Segmentation of positron emission tomography (PET) images is an important objective because accurate measurement of signal from radio-tracer activity in a region of interest is critical for disease treatment and diagnosis. In this study, we present the use of a graph based method for providing robust, accurate, and reliable segmentation of functional volumes on PET images from standardized uptake...
The article deals with initial research into the methods of microscopic image processing methods for the study of diverticular disease. Functional and structural changes were found in tissues of persons afflicted with diverticular disease in previous medical research. The acquired images were processed manually. The goal of research into image processing methods for automatic or semiautomatic processing...
This paper describes ViewFinder Medicine (vfM) as an application of content-based image retrieval to the domain of Alzheimer's disease and medical imaging in general. The system follows a multi-tier architecture which provides the flexibility in experimenting with different representation, classification, ranking and feedback techniques. Classification is central to the system because besides providing...
Computed tomography (CT) technology helps us to acquire high resolution, isotropic images of the lungs in a single breath hold. Analysis of these large volumes of data manually is very time consuming and tedious. Automation of analysis of the CT images is therefore vital in the study of CT images. This paper reviews the literature on computer analysis of the lungs in CT scans addressing segmentation...
Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed...
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