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This paper proposes a new method for melasma pigmentary area segmentation utilizing re action-diffusion based level set model (RDLSM) together with local entropy thresholding. In the adopted level set model, a diffusion term is used to regularize the level set function while a reaction term with anticipated sign property is used to force the zero level set towards desired locations. Then local entropy...
This paper discusses about a method adopted to develop a computer-aided diagnostic system to achieve automatic detection and classification of liver lesions. The procedure followed consists of first segmenting the CT scan image so as to accurately extract out the lesion region alone from the rest of the abdominal details. This Region Of Interest(ROI) is now used up for extracting out first order and...
Diabetes, hypertension, cerebral arteriosclerosis and other diseases have become great threats to human health, so it is urgent to explore their initial symptoms for early prevention and treatment. As an important part of small and medium-sized vessels of human body, retinal vessel is the only deep capillary that can be non-traumatic directly observed and its morphology, such as vascular diameter,...
Accurate segmentation of skin lesion is one of the most important step for automated diagnosis of skin cancer. Various characteristics of skin lesions and intensity variations in images can make it a highly challenging task. A new histogram analysis based fuzzy C mean thresholding method is presented here. It unifies the advantages of soft and hard thresholding algorithms along with reducing the computational...
Segmentation is an important step for the diagnosis of multiple sclerosis. This paper presents a new approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. At first, Brain image is considered to be three parts, namely the dark, the gray, and the white part. Then, the fuzzy regions of their member functions are determined...
A Hybrid Particle Swarm Optimization algorithm that incorporates a Wavelet theory based mutation operation is used for segmentation of Magnetic Resonance Images. We use Entropy maximization using Hybrid Particle Swarm algorithm with Wavelet based mutation operation to get the region of interest of the Magnetic Resonance Image. It applies the Multi-resolution Wavelet theory to enhance the Particle...
We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent...
The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed...
The main objective of this research is to develop a prototype system for diagnosing paddy diseases, which are blast disease (BD), brown-spot disease (BSD), and narrow brown-spot disease (NBSD). This paper concentrates on extracting paddy features through off-line image. The methodology involves image acquisition, converting the RGB images into a binary image using automatic thresholding based on local...
This paper proposes automated detection of skin lesions by unsupervised feature based clustering based on a new fuzzy entropy function for characterizing texture. The parameterized entropy function is optimized using the Bacterial Foraging algorithm. The clustering of the entropy function of the image is done using the popular Fuzzy C-means algorithm (FCM). The experimental results obtained after...
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