An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images |
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| Affiliation: | 1. Department of ICE, Kalasalingam University, India;2. Department of Electronics and Communication Engineering, Kalasalingam University, India;3. Gopalan College of Engineering and Management, Bangalore, India;4. KLN College of Engineering, India;1. Faculty of Science, Ain Shams University, Postal code 11566 Cairo, Egypt;2. Egyptian E-Learning University, 33 El-mesah St., El-Dokki, Postal code 12611 Giza, Egypt;3. Faculty of Computers and Information Technology, Future University, Cairo, Egypt;4. The School of Computer Science, University of Westminster, London HA1 3TP, UK;5. Faculty of Informatics and Computer Science, British University of Egypt, Cairo, Egypt;6. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt;1. Department of CSE, Jawaharlal Nehru Technological University, Kakinada, India;2. Department of ECE, Jawaharlal Nehru Technological University, Kakinada, India;3. Department of CSE, Acharya Nagarjuna University, Guntur, India;1. Mahatma Gandhi Institute of Technology, Hyderabad, India;2. Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India;3. JNTUK-UCEV, Andhra Pradesh, India |
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| Abstract: | Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository. |
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| Keywords: | SOM (self-organizing map) FKM (fuzzy K-means algorithm) MR brain tumor identification Tissue segmentation Peak signal to noise ratio (PSNR) Jaccard Index Dice Overlap Index (DOI) Sensitivity Specificity Mean square error (MSE) Computational time |
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