Implementation of Brain Tumor Segmentation in brain MR Images using K-Means Clustering and Fuzzy C-Means Algorithm

Abstract

This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this work uses computer aided method for segmentation (detection) of brain tumor based on the k.means and fuzzy c-means algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis.

A comprehensive review: Segmentation of MRI images-brain tumor

“…In the medical diagnosis of tumors, standard treatments are available for different characteristics in different diseases. Examination of tumors in different sizes and stages are accurately improved by combining k means and FCM segmentation algorithms (Gupta and Shringirishi, ). Segmentation of brain tumor using FCM has become an effective research area and it is given as, where y is the degree of fuzziness in clustering (in general y = 2), r is the membership of fuzzy data to as center of the cluster and, d is the distance between data, and cluster center j , .…”

“…Some of the hybrid methods are: Combination of multi‐region with multi‐reference framework to obtain lower standard deviations and higher tissue overlapping rates (Phillips et al, ). Combination of EM segmentation and active contours with binary mathematical morphology is used to segment adult brain using 2D MRI for segmenting different brain tissues (Kapur et al, ). Combining thresholding, active contours with T1 and T2 weighted MRI the volume of newborn brain can be segmented (Despotovic et al, ). Support Vector Machines are combined with the conditional random field to achieve low computational times of segmentation with multispectral datasets among different patients (Bauer et al, a, ). Based on ANN hybrid segmentation of T2 and FLAIR MRI is proposed (Vijayakumar and Chandrashekhar Gharpure, ) to segment normal tissues, edema, cysts, and tumor lesions. Combination of kernel feature selection with SVM is used achieve to low computational time and better results in testing T1W and T2W MRI (Zhang et al, ). Combining K‐means with FCM is used to obtain better reproducibility and accurate results (Gupta and Shringirishi, ). Self‐organizing maps are combined with entropy‐gradient clustering method to improve brain segmentation in MRI images (Ortiz et al, ). Integration of modified Particle Swarm Optimization with fuzzy entropy based segmentation provides the maximum entropy while segmenting tumors in brain with less computation time (Remamany et al, ). …”

“…When growth of the cancer cells is uncontrollable then the disease is named as the tumor. This tumor is of different types and has different characteristics, which are cured with various types of treatments (Gupta and Shringirishi, ). Medical image segmentation of brain is the labeling process indicating tissue type or anatomical structure of each pixel/voxel.…”

Automated brain tumour segmentation techniques— A review

“…The tumour occurs in various parts of the body in which the total system is collapsed, when it is in the brain. The brain tumour has different shape and size and requires different treatment (Gupta and Shringirishi, ). Over 120 different types of brain tumour exist and can be classified as primary and metastatic brain tumour.…”

Survey on Segmentation of Brain Tumor: A Review of Literature

“…A knowledge-based fuzzy clustering approach was proposed and implemented for the segmentation of the MRI images of brain tumor followed by 3-D connected components to build the tumor shape [19]. To improve the accurate detection of stage and size of tumor, a combined method of the k-means and fuzzy c-means algorithms was proposed to deal with the segmentation of brain tumor [20]. The disadvantages regarding this technique are: (1) It has a takes more computational time (2) It is more sensitive to noise.…”