Generalized Kohonen’s competitive learning algorithms for ophthalmological MR image segmentation Karen Chia-Ren Lin a , Miin-Shen Yang b, *, Hsiu-Chih Liu c , Jiing-Feng Lirng d , Pei-Ning Wang c a Department of Management Information System, Nanya Institute of Technology, Chung-Li, Taiwan b Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan c Neurological Institute, National Yang-Ming University and Taipei Veterans General Hospital, Taipei, Taiwan d Department of Radiology, National Yang-Ming University and Taipei Veterans General Hospital, Taipei, Taiwan Received 4 January 2003; received in revised form 25 April 2003; accepted 26 April 2003 Abstract Kohonen’s self-organizing map is a two-layer feedforward competitive learning network. It has been used as a competitive learning clustering algorithm. In this paper, we generalize Kohonen’s competitive learning (KCL) algorithm with fuzzy and fuzzy-soft types called fuzzy KCL (FKCL) and fuzzy-soft KCL (FSKCL). These generalized KCL algorithms fuse the competitive learning with soft competition and fuzzy c-means (FCM) membership functions. We then apply these generalized KCLs to MRI and MRA ophthalmological segmenta- tions. These KCL-based MRI segmentation techniques are useful in reducing medical image noise effects using a learning mechanism. They may be particularly helpful in clinical diagnosis. Two real cases with MR image data recommended by an ophthalmologist are examined. First case is a patient with Retinoblastoma in her left eye, an inborn malignant neoplasm of the retina frequently metastasis beyond the lacrimal cribrosa. The second case is a patient with complete left side oculomotor palsy immediately after a motor vehicle accident. Her brain MRI with MRA, skull routine, orbital CT, and cerebral angiography did not reveal brainstem lesions, skull fractures, or vascular anomalies. These generalized KCL algorithms were used in segmenting the ophthalmological MRIs. KCL, FKCL and FSKCL comparisons are made. Overall, the FSKCL algorithm is recommended for use in MR image segmentation as an aid to small lesion diagnosis. © 2003 Elsevier Inc. All rights reserved. Keywords: Kohonen’s competitive learning network; Fuzzy clustering; Fuzzy-soft method; Magnetic resonance image (MRI); Image segmentation 1. Introduction The medical image segmentation techniques provide im- portant diagnostic tools for the clinician. In general, medical images are obtained using different acquisition methods, including x-ray computer tomography (CT), single photon emission tomography (SPET), positron emission tomogra- phy (PET), ultra-sound (US), magnetic resonance image (MRI) and magnetic resonance angiographies (MRA), etc. MRI systems are important in medical image analysis. MRI has the multidimensional nature of data provided from ei- ther one of two different pulse sequences. The first pulse sequence is a spin echo pulse sequence that uses a 90 degree excitation pulse followed by one or more 180 degree re- phrasing pulses to generate one or more spin echoes. The second pulse sequence is a gradient echo pulse sequence that utilizes RF excitation pulse flips through any angle (not just 90 degree). The gradient rephrases the magnetic mo- ments so that a signal can be received by a coil and called a gradient echo. The timing parameters for the pulse se- quence, i.e., repetitive time (TR), echo time (TE) and RF excitation pulse provide sequence information from three types of tissue-dependent parameters; T1 (the spin-lattice relaxation time), T2 (the spin-spin relaxation) and PD (the proton density). However, there is always some degree of T2 weighting presented on any image due to the absence of a 180 degree rephrasing pulse. The gradient echo sequence allows for a reduction in the scan time as the TR is greatly reduced. The gradient echo sequence may be more useful than a spin echo pulse sequence to produce dynamic con- * Corresponding author. Tel.: +886-3-2653100; fax: +886-3- 2653160. E-mail address: msyang@math.cycu.edu.tw (M.-S. Yang). Magnetic Resonance Imaging 21 (2003) 863– 870 0730-725X/03/$ – see front matter © 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0730-725X(03)00185-1