Brain Tumor Detection and Classification from MRI Images

A brain tumor is detected and classified by biopsy that is conducted after the brain surgery. Advancement in technology and machine learning techniques could help radiologists in the diagnosis of tumors without any invasive measures. We utilized a deep learning-based approach to detect and classify the tumor into Meningioma, Glioma, Pituitary tumors. We used registration and segmentation-based skull stripping mechanism to remove the skull from the MRI images and the grab cut method to verify whether the skull stripped MRI masks retained the features of the tumor for accurate classification. In this research, we proposed a transfer learning based approach in conjunction with discriminative learning rates to perform the classification of brain tumors. The data set used is a 3064 T MRI images dataset that contains T1 flair MRI images. We achieved a classification accuracy of 98.83%, 96.26%, and 95.18% for training, validation, and test sets and an F1 score of 0.96 on the T1 Flair MRI dataset

Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts

ISSN:0897-1889ISSN:1618-727

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55 approximately 90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18 approximately 96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5 approximately 18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.published_or_final_versio

Texture Feature Abstraction Based on Assessment of HOG and GLDM Features for Diagnosing Brain Abnormalities in MRI Images

Recognition of vehicles has always been a desired technology for curbing the crimes done with the help of vehicles Number imprinted on plates of cars and motorbikes are consist of numerals and alphabets and these plates can be easily recognized The uniqueness of combination of characters and numbers can be easily utilized for multiple purposes For instance fines can be imposed on people automatically for wrong parking toll fee can be automatically collected just by recognizing the number plate apart from these two there may be several numbers of uses can be accommodated Computer vision is comprehended as a sub space of the computerized reasoning furthermore software engineering fields Alternate ranges most firmly identified with computer vision are picture handling picture examination and machine vision As an exploratory order computer vision is apprehensive with the counterfeit frameworks that concentrate data from pictures and recordings The picture information can take numerous structures for instance segmentations of videos taken from several cameras This thesis presents a training based approach for the recognition of vehicle number plate The whole process has been divided into three stages i e capturing the image plate localization and recognition of digits over the plate The characteristics of HOG have been utilized for training and SVM has been used for adopted for classifying while recognizing This algorithm has been checked for more than 100 picture

Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts

In this paper, we propose a novel technique for skull stripping of infant (neonatal) brain magnetic resonance images using prior shape information within a graph cut framework. Skull stripping plays an important role in brain image analysis and is a major challenge for neonatal brain images. Popular methods like the brain surface extractor (BSE) and brain extraction tool (BET) do not produce satisfactory results for neonatal images due to poor tissue contrast, weak boundaries between brain and non-brain regions, and low spatial resolution. Inclusion of prior shape information helps in accurate identification of brain and non-brain tissues. Prior shape information is obtained from a set of labeled training images. The probability of a pixel belonging to the brain is obtained from the prior shape mask and included in the penalty term of the cost function. An extra smoothness term is based on gradient information that helps identify the weak boundaries between the brain and non-brain region. Experimental results on real neonatal brain images show that compared to BET, BSE, and other methods, our method achieves superior segmentation performance for neonatal brain images and comparable performance for adult brain image

Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans

Skull Stripping is a requisite preliminary step in most diagnostic neuroimaging applications. Manual Skull Stripping methods define the gold standard for the domain but are time-consuming and challenging to integrate into processing pipelines with a high number of data samples. Automated methods are an active area of research for head MRI segmentation, especially deep learning methods such as U-Net architecture implementations. This study compares Vanilla, Residual, and Dense 2D U-Net architectures for Skull Stripping. The Dense 2D U-Net architecture outperforms the Vanilla and Residual counterparts by achieving an accuracy of 99.75% on a test dataset. It is observed that dense interconnections in a U-Net encourage feature reuse across layers of the architecture and allow for shallower models with the strengths of a deeper network.Comment: Research Article submitted to the 2nd International Conference on Biomedical Engineering Science and Technology: Roadway from Laboratory to Market, at the National Institute of Technology Raipur, Chhattisgarh, Indi