An efficient clustering based texture feature extraction for medical image

Mohamed, Marghny H.; Abdelsamea, Mohammed M.

doi:10.1109/iccitechn.2008.4803114

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Data/dimensionality reduction, which is performed by intelligently changing the image from the lowest level of pixel data into higher level representations, is a key component in image analysis. We can extract relevant information from these representations through a process known as feature extraction [4].…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

Feature Extraction Methods for CT-Scan Images Using Image Processing

Bharodiya¹

2022

Computed-Tomography (CT) Scan

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Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. Once the CT scan image is captured, Doctors diagnose it to detect abnormal or normal condition of the captured of the patient’s body. In the computerized image processing diagnosis, CT-scan image goes through sophisticated phases viz., acquisition, image enhancement, extraction of important features, Region of Interest (ROI) identification, result interpretation etc. Out of these phases, a feature extraction phase plays a vital role during automated/computerized image processing to detect ROI from CT-scan image. This phase performs scientific, mathematical and statistical operations/algorithms to identify features/characteristics from the CT-scan image to shrink image portion for diagnosis. In this chapter, I have presented an extensive review on “Feature Extraction” step of digital image processing based on CT-scan image of human being.

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Section: Feature Extraction Techniquesmentioning

confidence: 99%

Feature Extraction Methods for CT-Scan Images Using Image Processing

Bharodiya¹

2022

Computed-Tomography (CT) Scan

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“…Those features are used to build the feature space of the images to teach and test our learning framework. Moreover, to improve the robustness and localization property of the represented features, a block wise partitioning method 12 has been applied. In the following section, we discuss in detail the development of our approach to classify images based on their feature representation.…”

Section: Mammographic Image Feature Representationmentioning

confidence: 99%

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

2019

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We propose a novel neural network approach for the classification of abnormal mammographic images into benign or malignant based on their texture representations. The proposed framework has the capability of mapping high dimensional feature space into a lowerdimension, in a supervised way. The main contribution of the proposed classifier is to introduce a new neuron structure for map representation and adopt a supervised learning technique for feature classification. This is achieved by making the weight updating procedure dependent on the class reliability of the neuron. We showed high accuracy (95.2%) for our proposed approach in the classification of abnormal real mammographic images when compared to other related methods.

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“…As a consequence, However, it is required to segment the images first as a pre-processing step before the feature extraction process. As a consequence, a bloc wise partitioning method [14] is used in this work, which can be described as follows (see Figure 1):…”

Section: Mammographic Image Representationmentioning

confidence: 99%

An Effective Image Feature Classiffication using an improved SOM

Abdelsamea,

Mohamed,

Bamatraf

2015

Preprint

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Image feature classification is a challenging problem in many computer vision applications, specifically, in the fields of remote sensing, image analysis and pattern recognition. In this paper, a novel Self Organizing Map, termed improved SOM (iSOM ), is proposed with the aim of effectively classifying Mammographic images based on their texture feature representation. The main contribution of the iSOM is to introduce a new node structure for the map representation and adopting a learning technique based on Kohonen SOM accordingly. The main idea is to control, in an unsupervised fashion, the weight updating procedure depending on the class reliability of the node, during the weight update time. Experiments held on a real Mammographic images. Results showed high accuracy compared to classical SOM and other state-of-art classifiers.

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An efficient clustering based texture feature extraction for medical image

Cited by 7 publications

References 17 publications

Feature Extraction Methods for CT-Scan Images Using Image Processing

Feature Extraction Methods for CT-Scan Images Using Image Processing

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

An Effective Image Feature Classiffication using an improved SOM

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