Improving Brain MR Image Classification for Tumor Segmentation using Phase Congruency

Latif, Ghazanfar; Iskandar, Dedi; Alghazo, Jaafar; Jaffar, Arfan

doi:10.2174/1573405614666180402150218

Cited by 13 publications

(5 citation statements)

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“…AANLIB dataset [30], available on Harvard Medical School website, consists of 90 flair modality MR Images with 62 normal and 28 tumorous images. PIMS (Pakistan Institute of Medical Sciences) dataset [28] consists of 258 T1 modality MR images including 144 normal and 114 tumorous images. The obtained results using these two datasets for all four aforementioned machine learning approaches are shown Table 6.…”

Section: E Results Validation and Comparisonmentioning

confidence: 99%

“…The selection of suitable classifier is dependent on the accuracy, performance and time complexity. The proposed hybrid features are tested using three different classifiers named; Multilayer Perceptron, Naïve Bayes and Random Forest [28]. The proposed hybrid features set got better results through all the classifiers but MLP got the highest accuracy.…”

Section: ) Classificationmentioning

confidence: 99%

“…In order to validate the results, two other datasets were used in the study. A dataset published by Harvard Medical School named AANLIB was used and consists of 90 MR images divided into 62 normal cases and 28 tumorous images [28]. Another dataset used for validation is the Pakistan Institute of Medical Sciences (PIMS) dataset that consists of 144 normal images and 114 tumorous images with a total size of 258 images [30].…”

Section: Experimental Datamentioning

confidence: 99%

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Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

et al. 2019

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Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classification of images. In this paper, an enhanced method is presented for glioma MR images classification using hybrid statistical and wavelet features. In the proposed method, 52 features are extracted using the first-order and second-order statistical features (based on the four MRI modalities: Flair, T1, T1c, and T2) in addition to the discrete wavelet transform producing a total of 152 features. The extracted features are applied to the multilayer perceptron (MLP) classifier. The results using the MLP were compared with various known classifiers. The method was tested on the dataset MICCAI BraTS 2015 which is a standard dataset used for research purposes. The proposed hybrid statistical and wavelet features produced 96.72% accuracy for high-grade glioma and 96.04% accuracy for low-grade glioma, which are relatively better compared to the existing studies. INDEX TERMS MRI classification, glioma tumor, hybrid statistical features, multilayer perceptron.

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Section: E Results Validation and Comparisonmentioning

confidence: 99%

Section: ) Classificationmentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

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Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

et al. 2019

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“…For a comprehensive analysis of brain tumors from MR images, different patterns of effective parts of the brain are required through which the tumorous part can be differentiated from the rest of the brain. A brain can be divided into three main parts; Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM) [ 25 ]. The important task during the segmentation of brain MR images is to partition these tissues correctly.…”

Section: Methodsmentioning

confidence: 99%

DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection

Latif

2022

Diagnostics

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The proper segmentation of the brain tumor from the image is important for both patients and medical personnel due to the sensitivity of the human brain. Operation intervention would require doctors to be extremely cautious and precise to target the brain’s required portion. Furthermore, the segmentation process is also important for multi-class tumor classification. This work primarily concentrated on making a contribution in three main areas of brain MR Image processing for classification and segmentation which are: Brain MR image classification, tumor region segmentation and tumor classification. A framework named DeepTumor is presented for the multistage-multiclass Glioma Tumor classification into four classes; Edema, Necrosis, Enhancing and Non-enhancing. For the brain MR image binary classification (Tumorous and Non-tumorous), two deep Convolutional Neural Network) CNN models were proposed for brain MR image classification; 9-layer model with a total of 217,954 trainable parameters and an improved 10-layer model with a total of 80,243 trainable parameters. In the second stage, an enhanced Fuzzy C-means (FCM) based technique is proposed for the tumor segmentation in brain MR images. In the final stage, an enhanced CNN model 3 with 11 hidden layers and a total of 241,624 trainable parameters was proposed for the classification of the segmented tumor region into four Glioma Tumor classes. The experiments are performed using the BraTS MRI dataset. The experimental results of the proposed CNN models for binary classification and multiclass tumor classification are compared with the existing CNN models such as LeNet, AlexNet and GoogleNet as well as with the latest literature.

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“…The rapid development of digital technology and the improvement in image technology are directly related. The most cutting-edge imaging technique is Magnetic Resonance Imaging (MRI) and it is used to visualize and picture the internal parts of the body [ 3 ]. The MRI scanner may provide precise anatomical data on soft tissues in the various parts of the human body with the help of a strong magnet.…”

Section: Introductionmentioning

confidence: 99%

Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis

et al. 2022

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Today, Magnetic Resonance Imaging (MRI) is a prominent technique used in medicine, produces a significant and varied range of tissue contrasts in each imaging modalities, and is frequently employed by medical professionals to identify brain malignancies. With brain tumor being a very deadly disease, early detection will help increase the likelihood that the patient will receive the appropriate medical care leading to either a full elimination of the tumor or the prolongation of the patient’s life. However, manually examining the enormous volume of magnetic resonance imaging (MRI) images and identifying a brain tumor or cancer is extremely time-consuming and requires the expertise of a trained medical expert or brain doctor to manually detect and diagnose brain cancer using multiple Magnetic Resonance images (MRI) with various modalities. Due to this underlying issue, there is a growing need for increased efforts to automate the detection and diagnosis process of brain tumor without human intervention. Another major concern most research articles do not consider is the low quality nature of MRI images which can be attributed to noise and artifacts. This article presents a Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to precisely handle the problem of low quality MRI images by eliminating noisy elements and enhancing the visible trainable features of the image. The enhanced image is then fed to the proposed PCNN to learn the features and classify the tumor using sigmoid classifier. To properly train the model, a publicly available dataset is collected and utilized for this research. Additionally, different optimizers and different values of dropout and learning rates are used in the course of this study. The proposed PCNN with Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm achieved an accuracy of 98.7%, sensitivity of 99.7%, and specificity of 97.4%. In comparison with other state-of-the-art brain tumor methods and pre-trained deep transfer learning models, the proposed PCNN model obtained satisfactory performance.

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Improving Brain MR Image Classification for Tumor Segmentation using Phase Congruency

Cited by 13 publications

References 0 publications

Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection

Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis

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