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This paper presents a survey of blob detection methods which has been applied on image processing with relation of medical images proposed by literature. “The blob detection is a mathematical method which detects regions or points in digital images”. [1] The regions or points which have noticeable difference with their surroundings is called blob. Given the increased interest in biomedical image processing...
Cancer has grown universally as a disease in recent years and it has proven to be the major cause of fatality in humans. The main focal point in this paper is on leukemia - a specific type of blood cancer. The disclosure of leukemia is done with the help of examination of the physical properties of the cells present in the bone marrow smear. This paper introduces peculiar detection of leukemia using...
Many algorithms for video stabilization have been proposed so far. However, not many digital video stabilization procedures for endoscopic videos are discussed. Endoscopic videos contain immense shakes and distortions as a result of some internal factors like body movements or secretion of body fluids as well as external factors like manual handling of endoscopic devices, introduction of surgical...
Anatomical landmark point are 3D points in a well-defined anatomical structure in which correspondences between and within the population of the anatomical structure are preserved. Accurate delineation of the landmark points is crucial task for many medical imaging applications. However, in most current clinical applications, the anatomical landmark points are usually manually delineated by experts,...
Purpose:To investigate the feasibility of utilizing texture features to classify nodule from normal thyroid tissue in Computed Tomography (CT) images. Materials and Methods: Group A (negative) includes 152 normal thyroid CT images from 55 patients healthy controls enrolled in the study. Group B (positive) includes 134 thyroid images with nodules (50 malignant, 84 benign) of 58 patients undergone thyroid...
Computer aided diagnosis (CAD) has been important more than ever for accurate diagnosis of liver tumors. The paper presents a novel image representation method for classifying normal livers and livers with tumors. It starts by capturing region of interesting (ROI) for individual livers, on which patches are extracted densely. Histogram of oriented gradients (HOG) and intensity are then extracted as...
Detection of implanted iodine-125 seeds in postoperative CT is a necessary step for evaluating the output of seed implantation brachytherapy of lung tumor. In this paper, we propose a semi-automated method to detect implanted seeds in postoperative lung CT. Three main steps are included in our approach. Firstly, the ROI (Region Of Interest) containing all seeds is extracted from the original image...
In this paper a method is presented for detection of melanin globules often present in melanocytic skin lesions images. The detection is done by performing image analysis similar to the one used in clinical evaluation. The method uses multi-stage image filtering to extract objects present in the dermoscopic image that match globule structure pattern. Classification of the found objects is made based...
Efficiently obtaining a reliable coronary artery centerline from computed tomography coronary angiography (CTCA) data is relevant in clinical practice. In this paper, open-snake is presented to extract the vessel centerline, which is drove by two external forces, one is Gradient Vector Flow (GVF), and the other one is an adaptive stretching force acted on the two ends of open-snake. To make the open-snake...
An automatic segmentation method of hippo-campus for volume measurement in MR brain images by using sparse patch representation and discriminative dictionary learning is proposed in this paper, which can overcome the limitation of multi-atlas approaches that mostly rely on similarity between target image and atlases for more accurate segmentation. In the proposed method, atlases are registered to...
The article presents an application of Adaptive Splitting and Selection (AdaSS) ensemble classifier in a real-life task of designing an efficient clinical decision support system for breast cancer malignancy grading. We approach the problem of cancer detection form a different angle - we already know that a given patient has a malignant type of cancer and we want to asses the level of that malignancy...
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...
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...
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