Segmentation of MR Images of the Brain to Detect WM, GM, and CSF Tissues in the Presence of Noise and Intensity Inhomogeneity

ABSTRACT

Segmentation of brain MR images for the detection of various healthy brain tissues such as white matter, gray matter, and cerebrospinal fluid is of immense interest to detect and to diagnose different brain-related disorders at the primitive level. MR image segmentation becomes a difficult task owing to the presence of intensity inhomogeneity (IIH), noise, partial volume effects, and intrinsic nature of the MR images. This paper proposes an efficient, region-based, energy minimization technique named as anisotropic multiplicative intrinsic component optimization (AMICO) to segment the brain image in the presence of IIH and noise and to detect different healthy brain tissues. The proposed algorithm utilizes a powerful anisotropic diffusion filter to denoise the image. The MICO algorithm segment the denoised image after correcting IIH. In the proposed technique, MR brain image is decomposed into two multiplicative and intrinsic components, such as the true image component and the bias field component. Brain tissue physical properties are represented by the component of true image and the IIH is characterized by the bias field component. Optimization of these two multiplicative and intrinsic components by employing the proposed effective energy minimization process, result in IIH correction and tissue segmentation simultaneously. The pursuance of the proposed technique is compared with some other existing techniques using the parameters, dice similarity coefficient, sensitivity, specificity, and segmentation accuracy. The results validated the excellent performance of the AMICO in detecting various brain tissues consisting of various levels of IIH and noise.

KEYWORDS:

• Anisotropic diffusion
• CSF
• GM
• Intensity inhomogeneity (IIH)
• MICO
• MRI
• WM

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Sandhya Gudise

Sandhya Gudise is a PhD student of Jawaharlal Nehru Technological University, Kakinada and working as an associate professor in Electronics and Communication Department in VNITSW, Guntur. She received the master's degree in instrumentation and control systems from JNTU College of Engineering, Kakinada. Her research interests focus on the medical image processing, with specific emphasis on the detection of normal and abnormal tissues in MR images of the brain.

Corresponding author E-mail: [email protected]

Giri Babu Kande

Giri Babu Kande is a professor in Electronics and Communication Department in VVIT, Guntur. He has teaching experience of about 20 years. He is guiding many UG, PG projects, and research scholars. His research interests include digital image processing, VLSI, and communication. He received the PhD degree in digital image processing from Jawaharlal Nehru Technological University, Hyderabad. He is a member of various professional chapters and published many research papers in various SCI journals and national and international conferences.

E-mail: [email protected]

Satya Savithri T.

Satya Savithri T is a professor in Electronics and Communication Department of Jawaharlal Nehru Technological University, Hyderabad. She has teaching experience of about 20 years. She received the PhD degree in image processing from Jawaharlal Nehru Technological University, Hyderabad. Her research interests include DIP, VLSI, microwaves, and communication. She is a member of ISTE, IEI, and published many research papers in various SCI journals and conferences. She is guiding many UG, PG, and funded projects and also 15 research scholars.

E-mail: [email protected]