{"id":1810,"date":"2015-12-28T11:36:27","date_gmt":"2015-12-28T11:36:27","guid":{"rendered":"http:\/\/biomedpharmajournal.org\/?p=1810"},"modified":"2020-04-25T03:33:34","modified_gmt":"2020-04-25T03:33:34","slug":"robust-classification-of-primary-brain-tumor-in-mri-images-based-on-multi-model-textures-features-and-kernel-based-svm","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol8no2\/robust-classification-of-primary-brain-tumor-in-mri-images-based-on-multi-model-textures-features-and-kernel-based-svm\/","title":{"rendered":"Robust Classification of Primary Brain Tumor in MRI Images Based on Multi Model Textures Features and Kernel Based SVM"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

Medical image segmentation has core importance to implement high level operations such as tissues recognition and classification. Segmentation of this type of imaging data is still a problem that demands to be probed [1]. Segmentation of medical structures is compulsory in computer assisted surgery or diagnosis system. Medical imaging methods take part in focusing organs and tissues during surgery process [2]. The visible and palpable tumor mass seen by different medical imaging modalities like CT and MRI is usually complicated to identify automatically with existing image processing methods, but still same is achieved manually by clinicians using computer aided drawing software. Segmentation of basic brain MR regions supports in visualization to identify various diseases, morphological and volume estimation, tissue classification and etc. Various methods of segmentation are edge based, thresholding, watershed and region growing etc., which are distinguished on the basis of their application and modality using which image is acquired [3].<\/p>\n

Magnetic Resonance Imaging (MRI) is a multi-sequence medical imaging technique with which stacks of images are acquired with different tissue contrasts. Each sequence, namely, T1-weighted,T2-weighted, Proton Density (PD), Fluid-Attenuated Inversion Recovery (FLAIR), etc., highlights specific properties of tissues and pathologies, but none of them can provide completely decisive and reliable information. In MR images, lesions usually appear quite different in texture from normal tissues. Texture features provide an important in the perception and discrimination of a tumor. Image classification is one of the typical computer applications widely used in the medical field, especially for abnormality detection in Magnetic Resonance (MR) brain images [4]. The automated image classification systems used for such applications must be significantly efficient in terms of accuracy since false detection may lead to fatal results.<\/p>\n

The process of extracting the original image based on the certain features which undergo a transformation of pixel regions. Feature extraction can be based on two approaches; they are human centered and machine centered approaches. Human centered approach measures with perception based features such as texture, and the perfect mathematical representation is selected for it. In machine centered approach, a unified computing scheme is selected for extracting certain ad-hoc features. There \u00a0are \u00a0many \u00a0techniques \u00a0for feature extraction, e.g. texture Features [5], Gabor features, feature based on wavelet transform, principal component analysis, minimum noise fraction transforms, \u00a0discriminant \u00a0analysis, \u00a0decision\u00a0 boundary feature extraction, non-parametric weighted feature extraction \u00a0and \u00a0spectral \u00a0mixture \u00a0analysis. The texture elements act as attributes based on the pixel region that digitizes the individual object into binary forms of information through computer imaging based on the silhouette of image information [6-7].<\/p>\n

Machine learning classification techniques, including supervised and unsupervised (clustering or fuzzy clustering)[8-9], are also introduced into brain tumor segmentation. Trained classifiers estimate the probability for each voxel in the testing volume, judging whether the voxel belongs to the target or the background. The threshold of the probability map is calculated to obtain the segmentation result or provide for post-processing. These techniques make it possible for high-dimensional features to be utilized in order to achieve a better discriminatory power for tumors compared with sole dependence on intensity information [10]. Moreover, the approaches applied in the field of pattern analysis can be transplanted into medical image segmentation, such as a distance metric learning algorithm, to make the intra-class samples closer while keeping extra-class samples as far away from each other as possible. Unfortunately, these classifications based segmentation approaches consider the voxels in the image to be independent of each other, with no spatial correlation both in the training and testing phases [11].<\/p>\n

Related Work<\/strong><\/p>\n

Lots of research has been performed for the segmentation of normal and abnormal tissues in MRI brain images. Some of the recent related works regarding the classification of brain tissues are reviewed in this section.<\/p>\n

Kekre et al. [8] have proposed a vector quantization segmentation technique to identify a cancerous mass from MRI images. In order to improve the radiologists\u2019 diagnostic performance, computer-aided diagnosis (CAD) scheme has been introduced to enhance the recognition of primary signatures of this disease: masses and micro calcifications. As well, to tackle the class distinguishability as well as feature space sparseness and solution space intricacy problems in multivariate image segmentation, a Markov random field (MRF) based multivariate segmentation algorithm called \u201cmultivariate iterative region growing using semantics\u201d (MIRGS) has been proposed by Shan Shen [13]. In MIRGS, the impact of intra-class variation and computational cost has been minimized by means of the MRF spatial context model integrated with adaptive edge penalty and applied to regions. To restrain the initialization sensitivity, a region-level means (RKM) based initialization technique has been utilized, which always provides exact initial conditions at low computational cost. Experiments have demonstrated the pre-eminence of RKM relative to two frequently used initialization techniques.<\/p>\n

As well, a template-based framework for multi-object segmentation of deep brain structures (caudate nucleus, putaen and thalaus) in medical brain images has been presented by Jue Wu and Albert C.S. Chung [14]. This framework combines the information of edge features, region statistics, and inter-structure constraints for identifying and locating all target brain structures. The multi-object template has been structured in the form of a hierarchical Markov Dependence Tree (MDT), and manifold objects have been successfully matched to a target image via a top-to-down optimization approach. The final segmentation has been achieved through refinement by a B-spline based non-rigid registration between the exemplar image and target image. The approach necessitates only one example as training data. The technique has been validated using a publicly available T1-weighted MRI database with expert-segmented brain structures and obtained satisfactory results as 0.80 Dice score for the caudate nuclei, 0.81 Dice score for the putaina, and 0.84 Dice score for the thalai on average.<\/p>\n

In order to enhance the performance of automated image segmentation, especially in the field of brain tissue segmentation from 3D MRI towards classical image deterioration including the noise and bias field artifacts that arise in the MRI acquisition process, caldairou et al. [15] have proposed to integrate into the FCM segmentation methodology concepts stimulated by the Non-Local (NL) framework. The major algorithmic contributions of this paper were the definition of an NL data term and an NL regularization term to effectively handle the intensity inhomogeneity and noise in the data. Then, the resulting energy formulation was built into an NL\/FCM brain tissue segmentation algorithm. Experiments carried out on both the synthetic and real MRI data, leading to the classification of brain tissues into gray-matter, white matter, and cerebro-spinal fluid, have shown a substantial enhancement in performance in the case of higher noise levels, when compared to a range of standard algorithms.<\/p>\n

Feature Extraction<\/strong><\/p>\n

The purpose of feature extraction is to reduce the original data set\u00a0 by\u00a0 measuring certain properties, or\u00a0 features, that distinguish one\u00a0 input pattern from another pattern [16].\u00a0 The extracted feature is expected to provide the characteristics of the input type to the classi\ufb01er by considering the description of the relevant properties of the image into a feature space. The proposed method,feature extraction method consists of Three steps<\/p>\n