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Clinical Decision Support (CDS) aids in early diagnosis of liver cancer, a potentially fatal disease prevalent in both developed and developing countries. Our research aims to develop a robust and intelligent clinical decision support framework for disease management of cancer based on legacy Ultrasound (US) image data collected during various stages of liver cancer. The proposed intelligent CDS framework...
Contrast enhancement is one of the important steps in image processing. Enhancement process has a vital role in medical image processing. Histogram Equalization (HE) plays the major role in enhancement process. HE is simple and effective method in contrast enhancement. The conventional HE enhancement process outputted an excessive contrast result. Which leads to poor classification result, especially...
Breast Cancer is one of the major health concerns of women all over the world. Computer Aided Detection (CAD) aids radiologists for the early detection of abnormalities in the breast masses. Abnormalities in the breast may be cancerous or non cancerous. This work proposes an effective CAD system that considerably reduces the misclassification rates of these abnormalities. 60 mammogram images were...
In this paper, we present a fully automated method for cell nuclei detection in Pap smear images. The locations of the candidate nuclei centroids in the image are detected with morphological analysis and they are refined in a second step, which incorporates a priori knowledge about the circumference of each nucleus. The elimination of the undesirable artifacts is achieved in two steps: the application...
Cervical cancer is one of the deadliest cancer known and is also a key research area in image processing. The main problem with this cancer is that it cannot be detected as it doesn't throw any symptoms until the final stages. This is attributed to the cancer itself and also to the lack of pathologists available to screen the cancer. Here we have proposed a novel approach to classify the various malignancies...
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main...
The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation...
Breast cancer is the most common cancer diagnosed among U.S. women. In this paper we have done some experiments for tumor detection in digital mammogram images. First of all, we have described a method that segments the breast image automatically. As a preprocessing, we have used fuzzy based noise removal filter that removes noise. Then for segmentation, we have provided a background removal method...
Recognition of prostate calculus is an important step to determine the source of pathological organ, and is of great importance for further diagnosis of prostate cancer. In this paper, due to some tissues are similar to calculus, and prostate calculus usually adheres to other tissues, a recognition algorithm for prostate calculus based on transition region and PCA-SVM is proposed. Firstly, local entropy,...
Mass in mammogram can be an indicator of breast cancer. In this work we propose a new approach using twin support vector machine (TWSVM) for automated detection of mass in digital mammograms. This algorithm finds two hyperplanes to classify data points into different classes according to the relevance between a given point and either plane. It works much faster than original SVM classifier. The proposed...
An automated method that detects early cancerous specimens based on image analysis is described. After acquisition and noise reduction, the microscope images are segmented into individual cell nucleus, from which the feature vectors of nucleus are calculated. The dimensionality of the feature vectors is then reduced using a method combing F-Score and random forest algorithms. The types of the cell...
The cervical cancer screening technology based on quantitative cytometry is studied. Feulgen stain is conducted on the sample of cervical tissues. Then the microscopic image of the sample is captured by CCD camera. The images of cell nucleuses are extracted by image segmentation. And the morphological, optical density and texture parameters of the cell nucleuses are calculated. The dimension of the...
In this paper, a new intelligent method of classifying benign and malignant melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median...
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