Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm

Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer’s. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.

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Second near-infrared (NIR-II) imaging: a novel diagnostic technique for brain diseases

Reviews in the Neurosciences ◽

10.1515/revneuro-2021-0088 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Na Xie ◽

Ya Hou ◽

Shaohui Wang ◽

Xiaopeng Ai ◽

Jinrong Bai ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Tumor ◽

Near Infrared ◽

Diagnostic Technique ◽

High Sensitivity ◽

Brain Diseases ◽

Tissue Penetration ◽

Tumor Inflammation

Abstract Imaging in the second near-infrared II (NIR-II) window, a kind of biomedical imaging technology with characteristics of high sensitivity, high resolution, and real-time imaging, is commonly used in the diagnosis of brain diseases. Compared with the conventional visible light (400–750 nm) and NIR-I (750–900 nm) imaging, the NIR-II has a longer wavelength of 1000–1700 nm. Notably, the superiorities of NIR-II can minimize the light scattering and autofluorescence of biological tissue with the depth of brain tissue penetration up to 7.4 mm. Herein, we summarized the main principles of NIR-II in animal models of traumatic brain injury, cerebrovascular visualization, brain tumor, inflammation, and stroke. Simultaneously, we encapsulated the in vivo process of NIR-II probes and their in vivo and in vitro toxic effects. We further dissected its limitations and following optimization measures.

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Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm

Machine Learning with Applications ◽

10.1016/j.mlwa.2021.100044 ◽

2021 ◽

pp. 100044

Author(s):

Md. Khairul Islam ◽

Md. Shahin Ali ◽

Md. Sipon Miah ◽

Md. Mahbubur Rahman ◽

Md. Shahariar Alam ◽

...

Keyword(s):

Principal Component Analysis ◽

Brain Tumor ◽

Clustering Algorithm ◽

Principal Component ◽

Component Analysis ◽

Tumor Detection ◽

Mr Image

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Detection of Tumor From Brain MRI Images Using Supervised and Unsupervised Methods

AI Innovation in Medical Imaging Diagnostics - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-7998-3092-4.ch003 ◽

2021 ◽

pp. 61-75

Author(s):

Kannan S. ◽

Anusuya S.

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Magnetic Resonance ◽

Brain Mri ◽

Tumor Location ◽

Magnetic Resonance Images ◽

Unsupervised Clustering ◽

Clustering Techniques ◽

Different Tissues ◽

Tumor Mri

Brain tumor discovery and its segmentation from the magnetic resonance images (MRI) is a difficult task that has convoluted structures that make it hard to section the tumor with MR cerebrum images, different tissues, white issue, gray issue, and cerebrospinal liquid. A mechanized grouping for brain tumor location and division helps the patients for legitimate treatment. Additionally, the method improves the analysis and decreases the indicative time. In the separation of cerebrum tumor, MRI images would focus on the size, shape, area, and surface of MRI images. In this chapter, the authors have focused various supervised and unsupervised clustering techniques for identifying brain tumor and separating it using convolutional neural network (CNN), k-means clustering, fuzzy c-means grouping, and so on.

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Brain Tumour Detection Using Deep Learning Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit195233 ◽

2019 ◽

pp. 169-175 ◽

Cited By ~ 2

Author(s):

P. Tamije Selvy ◽

V. P Dharani ◽

A. Indhuja

Keyword(s):

Neural Network ◽

Image Processing ◽

Brain Tumor ◽

Brain Tumors ◽

Probabilistic Neural Network ◽

Brain Mri ◽

Histogram Equalization ◽

Tumor Location ◽

World Health ◽

Basic Part

In recent years the occurrence of brain tumor has exaggerated in large amount among the people. Gliomas are one of the most common types of primary brain tumors that represent 30% of all human brain tumors and 80% of all malevolent tumors. The grading system specified by the World Health Organization (WHO) is deployed as a standard mechanism for medical diagnosis, prognosis, and the existence forecast so far. The main ideology of this paper is to propose and develop reliable and typical methods to detect the brain tumor, extract the characteristic of it and classify the glioma using Magnetic Resonance Imaging (MRI). The developed model helps in the detection of brain tumor automatically and it is implemented using image processing and artificial neural network. The most basic part of image processing is the analysis and manipulation of a digitized image, especially in order to improve its quality. In this proposed system, the Histogram Equalization (HE) technique is used to improve the contrast of the original image. Then the pre-processed image is subjected to feature extraction using Gray Level Co-occurrence Matrix (GLCM). The obtained feature is given to Probabilistic Neural Network (PNN) classifier that is used to train and test the performance accuracy in the perception of tumor location in brain MRI images. By implementing this approach, PNN classifier has procured accuracy of about 90.9%.

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Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

10.31399/asm.cp.istfa2018p0550 ◽

2018 ◽

Author(s):

Peter De Wolf ◽

Zhuangqun Huang ◽

Bede Pittenger

Keyword(s):

Single Point ◽

Electrical Characterization ◽

Principal Component ◽

High Sensitivity ◽

Data Cube ◽

Nanometer Scale ◽

Learning Approaches ◽

Data Set ◽

3D Data ◽

Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

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Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666210104111218 ◽

2021 ◽

Vol 16 ◽

Author(s):

Ghazanfar Latif ◽

Jaafar Alghazo ◽

Fadi N. Sibai ◽

D.N.F. Awang Iskandar ◽

Adil H. Khan

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Brain Mri ◽

Tumor Segmentation ◽

Brain Anatomy ◽

Mri Segmentation ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Low Contrast ◽

Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

Journal of Digital Imaging ◽

10.1007/s10278-020-00400-7 ◽

2021 ◽

Author(s):

Pooja Prabhu ◽

A. K. Karunakar ◽

Sanjib Sinha ◽

N. Mariyappa ◽

G. K. Bhargava ◽

...

Keyword(s):

Large Scale ◽

Brain Mri ◽

Similarity Measures ◽

Principal Component ◽

Brain Anatomy ◽

Morphological Operations ◽

Mr Images ◽

Tilt Correction ◽

Brain Mr Images ◽

The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

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