Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

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Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.

Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey

Classification of brain tumor is the heart of the computer-aided diagnosis (CAD) system designed to aid the radiologist in the diagnosis of such tumors using Magnetic Resonance Image (MRI). In this paper, we present a framework for classification of brain tumors in MRI images that combines statistical features and neural network algorithms. This algorithm uses region of interest (ROI), i.e. the tumor segment that is identified either manually by the technician/radiologist or by using any of the ROI segmentation techniques. We focus on feature selection by using a combination of the 2D Discrete Wavelet Transform (DWT) and 2D Gabor filter techniques. We create the features set using a complete set of the transform domain statistical features. For classification, back propagation neural network classifier has been selected to test the features selection impact. To do so, we used a large dataset consisting of 3,064 slices of T1-weighted MRI images with three types of brain tumors, Meningioma, Glioma, and Pituitary tumor. We obtained a total accuracy of 91.9%, and specificity of 96%, 96.29%, and 95.66% for Meningioma, Glioma, and Pituitary tumor respectively. Experimental results validate the effectiveness of the features selection method and indicate that it can compose an effective feature set to be used as a framework that can be combined with other classifications technique to enhance the performance.

Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network

Facial aging adversely impacts performance of face recognition and face verification and authentication using facial features. This stochastic personalized inevitable process poses dynamic theoretical and practical challenge to the computer vision and pattern recognition community. Age estimation is labeling a face image with exact real age or age group. How do humans recognize faces across ages? Do they learn the pattern or use age-invariant features? What are these age-invariant features that uniquely identify one across ages? These questions and others have attracted significant interest in the computer vision and pattern recognition research community. In this paper, we present a thorough analysis of recent research in aging and age estimation. We discuss popular algorithms used in age estimation, existing models, and how they compare with each other; we compare performance of various systems and how they are evaluated, age estimation challenges, and insights for future research.

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Age estimation via face images: a survey

This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.

Automated Segmentation of Human Brain MR Images Aided by Fuzzy Information Granulation and Fuzzy Inference

Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal) may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.

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GLCM Textural Features for Brain Tumor Classification

structure of the human body obtained by different modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc. Images from these modalities are generally used for the diagnosis purpose. In the Brain MRI image, the tumor part can be seen clearly but the physician need the accurate size and the correct measurement for the treatment. This requires to segment the tumor part from the given Brain MRI image and quantify it. We have used Brain tumor (Metastatic bronchogenic carcinoma) MRI scans of 42-year-old woman from the Whole Brain Atlas (WBA) for the experiment; in this paper, we presented the basic level set method to catch the boundaries of the tumor area in 2D MRI images. After catching the accurate boundaries, we applied the inverse threshold and binary mask to segment the exact tumor region from the given different MRI brain scans. At last, we applied some of the statistical function on the region of interest (ROI) to quantify the tumor region.

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Level set and Thresholding for Brain Tumor Segmentation

Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. Segmentation plays the vital role in the medical image processing. There are several typical MRI segmentation approaches such as Thresholding techniques, Edge-based methods, Region-based segmentation, Cooperative hierarchical computation approach, Statistical approaches, and ANN image segmentation techniques. One more important phase in the medical sciences is Brain tumor classification, the images acquired from different modalities such as CT, MR that should be verified by the physician for the further treatment, but the manual classification of the MR images is the challenging and time consuming task. The main aim of this research and review paper is to explore the existing segmentation and classification techniques in the medical

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N. S. Zulpe

Papers

GLCM Textural Features for Brain Tumor Classification

Level set and Thresholding for Brain Tumor Segmentation

Review on Brain Tumor Segmentation and Classification Techniques

A Dorsal Hand Vein Pattern Recognition using Invariant Moment