Premium
Brain image segmentation using a combination of expectation‐maximization algorithm and watershed transform
Author(s) -
Kwon GooRak,
Basukala Dibash,
Lee SangWoong,
Lee Kun Ho,
Kang Moonsoo
Publication year - 2016
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22181
Subject(s) - image segmentation , artificial intelligence , thresholding , scale space segmentation , watershed , segmentation , computer science , morphological gradient , segmentation based object categorization , pattern recognition (psychology) , maxima and minima , mathematical morphology , algorithm , computer vision , mathematics , image (mathematics) , image processing , mathematical analysis
Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over‐segmentation is its most significant limitation. Therefore, this article proposes a combination of watershed transformation and the expectation‐maximization (EM) algorithm to segment MR brain images efficiently. The EM algorithm is used to form clusters. Then, the brightest cluster is considered and converted into a binary image. A Sobel operator applied on the binary image generates the initial gradient image. Morphological reconstruction is applied to find the foreground and background markers. The final gradient image is obtained using the minima imposition technique on the initial gradient magnitude along with markers. In addition, watershed segmentation applied on the final gradient magnitude generates effective gray matter and cerebrospinal fluid segmentation. The results are compared with simple marker controlled watershed segmentation, watershed segmentation combined with Otsu multilevel thresholding, and local binary fitting energy model for validation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 225–232, 2016