Segmentation of Multispectral Magnetic Resonance Image Using Penalized Fuzzy Competitive Learning Network

Lin, Jzau-Sheng; Cheng, Kuo‐Sheng; Mao, Chi

doi:10.1006/cbmr.1996.0023

Cited by 47 publications

(25 citation statements)

References 28 publications

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“…A variety of approaches has been attempted to achieve automated segmentation [8] including techniques dependent on simple thresholding, [17] edge extraction, [24] clustering algorithms, [35] elastic matching, [47] fuzzy logic, [30] knowledge bases, [7] region growing, [18] and pattern recognition. [41] Clearly, the multiplicity of approaches devised is a byproduct of the intense research into the field of artificial intelligence.…”

Section: Segmentation Processmentioning

confidence: 99%

Virtual endoscopic environments in modern neurosurgical practice

Levy¹,

Chen²,

Amar³

et al. 1999

FOC

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Modern radiographic techniques have allowed the creation of high-definition planar images that can provide important anatomical as well as physiological data. Planar imaging sets can be reformatted into three-dimensional (3-D) data sets that can then be manipulated to demonstrate important anatomical or gross pathological features. Three-dimensional data sets have been used with success in modern image-guided or frameless stereotactic surgery. Another potential application is so-called "virtual endoscopy" or "scopeless endoscopy," in which a 3-D anatomical data set is reformatted into a volume-rendered image that can then be viewed. By reformatting images in this way, a "surgeon's-eye" view can be obtained, which can aid in presurgical planning and diagnosis. The use of virtual endoscopy has the potential to increase our understanding of the appropriate anatomy and the anatomical relationships most apparent during neurosurgical approaches. In so doing, virtual endoscopy may serve as an important means of planning for therapeutic interventions. On the other hand, one must always be cognizant of the technical limitations of these studies regardless of the quality of the reconstructed images. Prospective, correlative, clinical studies in which the anatomical advantages of virtual-based endoscopy are evaluated in large cadaver or patient series must be performed. Until then, the only potential ways to compensate for errors that exist in the algorithms and reconstructions of 3-D endoscopic images are based on the surgeon's understanding of the clinical state of the patient and prior experience with the anatomy in the region of question.

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Section: Segmentation Processmentioning

confidence: 99%

Virtual endoscopic environments in modern neurosurgical practice

Levy¹,

Chen²,

Amar³

et al. 1999

FOC

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“…Image segmentation concentrates object or regions of interest (ROI) and it has a crucial part in therapeutic picture examination and interpretation. It is utilized for diagnosis, treatment planning and in monitoring treatment reaction (Lin, 1996, Ibrahim, 2010.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis System for Classification of Multi- Class Brain Tumor in MRI Images using Hybrid Texton Structure Descriptor and Particle Swam Optimized Neural Network

2016

Technical Journal of the Faculty of Engineering

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Abstract:Computer-Aided Diagnosis (CAD) is in wide practice in clinical work for the detection and prognosis of various types of abnormalities. For this, medical images acquired in various tests by utilizing diverse imaging modalities are used. In medical imaging, detecting and classifying the brain tumors in Magnetic Resonance Image (MRI) is a demanding and critical task. MRI gives anatomical structure's information and the potential abnormal tissues' information. Thus, this paper proposes a new system for MRI brain tumor detection and classification for multi class tumor images. The proposed system comprises feature extraction and classification. In feature extraction, the attribute of the co-occurrence matrix and the histogram is represented within this feature vector. In classification, particle swam optimization neural network is trained and tested to perform automatic classification of four different types of brain tumor.The method was applied on a population of 102 brain tumors histologically diagnosed as Meningioma (115), Metastasis (120), Gliomas grade II (65) and Gliomas grade II (70). Classification accuracy of proposed system in class 1(Meningioma) type tumor is 98.6%, class 2 (Metastasis) is 99.29%, class 3(Gliomas grade II) is 97.87 and class 4(Gliomas grade III) is 98.6%.

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“…In real medical diagnosis, oncologists, radiologists, and other medical experts are taking a large amount of time to segment medical images for a preparation of effective treatments. MRI systems can produce a number of images, each of which highlights the different fundamental & A. Jayachandran ajaya1675@gmail.com G. Kharmega Sundararaj kharmegam@gmail.com parameters of internal anatomical structures in the same body section with multiple contrasts [3]. Feature extraction is one of the most important methods for capturing visual content of an image.…”

Section: Introductionmentioning

confidence: 99%

Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM

Jayachandran

Sundararaj

2015

Int. J. Fuzzy Syst.

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Image classification is one of the typical computational applications widely used in the medical field, especially for abnormality detection in magnetic resonance (MR) brain images. Medical image classification is a pattern recognition technique in which different images are categorized into several groups based on some similarity measures. One of the significant applications is the tumor type identification in abnormal MR brain images. The proposed multi-class brain tumor classification system comprises feature extraction and classification. In feature extraction, the attributes of the co-occurrence matrix and the histogram are represented within the feature vector. In this work, the advantage of both co-occurrence matrix and histogram to extract the texture feature from every segment is used for better classification. In classification, the fuzzy logic-based hybrid kernel is designed and applied to train the support vector machine for automatic classification of four different types of brain tumors such as Meningioma, Glioma, Astrocytoma, and Metastases. Based on the experimental results, the proposed brain tumor classification method is more robust than other traditional methods in terms of the evaluation metrics, sensitivity, specificity, and accuracy.

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Segmentation of Multispectral Magnetic Resonance Image Using Penalized Fuzzy Competitive Learning Network

Cited by 47 publications

References 28 publications

Virtual endoscopic environments in modern neurosurgical practice

Virtual endoscopic environments in modern neurosurgical practice

Computer-Aided Diagnosis System for Classification of Multi- Class Brain Tumor in MRI Images using Hybrid Texton Structure Descriptor and Particle Swam Optimized Neural Network

Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM

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