Tumor Segmentation by a Self-Organizing-Map based Active Contour Model (SOMACM) from the Brain MRIs

Abstract

Segmentation of tumors from the brain Magnetic Resonance Images (MRIs) is very important for the analysis and right treatment. Tumors treated at early stages improve the survival time. This paper proposes an advanced method named SOMACM which is a combination of Self -Organizing -Map (SOM) and Active Contour Model (ACM) for the efficient segmentation of brain MRIs to detect tumors. ACM is an energy-based segmentation method and treats the segmentation as an optimization issue. It can model complex shapes and handles topological changes in the object boundary. The customary ACMs rely upon the intensities of the pixels and are very vulnerable to parameter tuning hence it is very difficult to segment the images of distinct pixel intensities. ACMs will evolve from the object boundary for the images consisting of Intensity Inhomogeneity (IIH). Neural Networks (NNs) are exceptionally compelling in processing the images of inhomogeneities. Furthermore, image segmentation can be done by NNs without the use of an objective function. The proposed SOMACM method works by precisely incorporating the global information extracted from the weights of the trained SOM neurons which helps in modeling complex shapes and distinct intensity distributions. It can handle images with noise, intensity similarity and IIH. The proposed segmentation technique is not sensitive to parameter tuning. The outcomes of the proposed SOMACM demonstrate the improved accuracy in the segmentation results of different types of tumor images, in contrast with the individual SOM, ACM, Fuzzy-C- Means (FCM), Particle Swarm Optimization (PSO) and Probabilistic Neural Networks (PNN) segmentation methods.

KEYWORDS:

• ACM
• Brain tumor
• FCM
• Medical image segmentation
• NNs
• SOM
• PNN
• PSO
• SOMACM

Additional information

Notes on contributors

G. Sandhya

Sandhya Gudise is received her PhD from 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. She published many research papers in various national and international journals.

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. Email: [email protected]

T. Satya Savithri

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. Email: [email protected]