An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI. (March 2017) - Roy, Sudipta; Bhattacharyya, Debnath; Bandyopadhyay, Samir Kumar; Kim, Tai-Hoon
Highlights: Accurate detection and segmentation of multiple sclerosis (MS) diseases with lesions positions identification. Adaptive background generation and binarization using global threshold are the key steps for MS lesions detection. Evaluates performance with other recent method. Proposed method produce good results visually as well as metrically. Proposed method reduced the under segmentation, over segmentation, and spurious lesions generation. Abstract: Background and objectives: Multiple sclerosis is one of the major diseases and the progressive MS lesion formation often leads to cognitive decline and physical disability. A quick and perfect method for estimating the number and size of MS lesions in the brain is very important in estimating the progress of the disease and effectiveness of treatments. But, the accurate identification, characterization and quantification of MS lesions in brain magnetic resonance imaging (MRI) is extremely difficult due to the frequent change in location, size, morphology variation, intensity similarity with normal brain tissues, and inter-subject anatomical variation of brain images. Methods: This paper presents a method where adaptive background generation and binarization using global threshold are the key steps for MS lesions detection and segmentation. After performing three phase level set, we add third phase segmented region with contour of brain to connect the normal tissues near the boundary. Then remove all lesions except maximum connected area and corpus callosum of the brain to generate adaptive background. The binarization method is used to select threshold based on entropy and standard deviation preceded by non-gamut image enhancement. The background image is then subtracted from binarized image to find out segmented MS lesions. Results: The step of subtraction of background from binarized image does not generate spurious lesions. Binarization steps correctly identify the MS lesions and reduce over or under segmentation. The average Kappa index is 94.88%, Jacard index is 90.43%, correct detection ration is 92.60284%, false detection ratio is 2.55% and relative area error is 5.97% for proposed method. Existing recent methods does not have such accuracy and low value of error rate both mathematically as well as visually due to many spurious lesions generation and over segmentation problems. Conclusions: Proposed method accurately identifies the size and number of lesions as well as location of lesions detection as a radiologist performs. The adaptability of the proposed method creates a number of potential opportunities for use in clinical practice for the detection of MS lesions in MRI. Proposed method gives an improved accuracy and low error compare to existing recent methods.