Brain Tumor Texture Analysis – Using Wavelets and Fractals

Paul, Tuhin Utsab; Ghosh, Aninda; Bandhyopadhyay, Samir Kumar

doi:10.11648/j.ijmi.20160404.11

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The value is fixed after the weights are adjusted on the training set. ANN's are known for classifying unknown input images . The delta rule will minimize an error term as below,

E_{p =} \frac{1}{2} \sum_{j} {(t_{italicpj} - o_{italicpj})}^{2}

…”

Section: Proposed Brain Cancer Classification Methodologymentioning

confidence: 99%

Early detection of brain cancer using association allotment hierarchical clustering

Tamilmani

Sivakumari

2019

Int J Imaging Syst Tech

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Medical applications uses the computer assisted diagnosis system for recognizing the enormous amount of diseases, but cancer is one of the major challenge to the doctors because it is difficult to recognize in earlier stage with accurate manner. To overcome these issues, in this article, an early detection of brain cancer using Association Allotment Hierarchical Clustering technique is proposed. The proposed automatic detection process includes various processing steps such as preprocessing, image segmentation, feature extraction, selection, and cancer identification process. Initially the microscopic images are captured which contaminated several noises that have been removed by applying the mutual piece‐wise linear transformation filtering approach. The method successfully eliminates irrelevant information and enhances the quality of the captured biopsy image. After that affected cancer cell region has been segmented with the help of Association Allotment Hierarchical Clustering method which examines the cell, tissues, and relevant border while segmenting the cancer cell. From the segmented region, different textures, statistical features are extracted depending on the tissue level, cell level, region, and contour level. Then to improve the performance of classification, the optimum features are selected using gray wolf optimization. The novelty of the proposed method is to give a better performance and the accuracy is obtained nearly 100%. Finally, the selected features are classified using neural network. Experimental result demonstrates that the performance of proposed method in terms of segmentation and computation time is better when compared with state‐of‐art approaches.

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“…The value is fixed after the weights are adjusted on the training set. ANN's are known for classifying unknown input images . The delta rule will minimize an error term as below,

E_{p =} \frac{1}{2} \sum_{j} {(t_{italicpj} - o_{italicpj})}^{2}

…”

Section: Proposed Brain Cancer Classification Methodologymentioning

confidence: 99%

Early detection of brain cancer using association allotment hierarchical clustering

Tamilmani

Sivakumari

2019

Int J Imaging Syst Tech

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RETRACTED CHAPTER: Texture Analysis in Skull Magnetic Resonance Imaging

Viloria

Hoz

Lezama

2020

Advances in Intelligent Systems and Computing

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raumatic brain injury (TBI) represents a serious public health problem worldwide. It is the most common cause of death and disability in the young population (aged 15-45 years), with major family, social and economic implications [1]. In medical terms, the human body can be studied as an object. The reconstruction of bone structures after physical damage generated by such an unfortunate event as disease or trauma can range from the implementation of prostheses to the engineering of artificial bone implants [2]. To make a virtual or physical model of any human anatomy, it must first be captured in three dimensions in a way that can be used by computational processes. Most hospital scanners capture data from the entire body both internally and externally. These machines are typically medical imaging devices capable of scanning the entire human body, among which, the most common is the magnetic resonance imaging (MRI) equipment [3]. The goal of the research is to analyze the texture in magnetic resonance imaging and its relationship to bone mineral content (BMC) using simple linear regression.

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Brain Tumor Texture Analysis – Using Wavelets and Fractals

Cited by 2 publications

References 6 publications

Early detection of brain cancer using association allotment hierarchical clustering

Early detection of brain cancer using association allotment hierarchical clustering

RETRACTED CHAPTER: Texture Analysis in Skull Magnetic Resonance Imaging

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