Medical Image Texture Segmentation Usingrange Filter

Rajaei, Amir

doi:10.5121/csit.2012.2125

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…However, no study was provided for choosing the optimum deterministic feature for segmentation, according to the evaluation of the references techniques used are named as as region-based, classification, or hybrid methods [28], and analyses the region. Additionally, the dataset can be segmented using sound statistical analysis, computational application, and confusion matrix [29]. In one referenced study, traditional segmentation technique that uses filters to remove noise from MR image boundaries [30].…”

Section: Literature Reviewsmentioning

confidence: 99%

Segmentation Method of Deterministic Feature Clustering for Identification of Brain Tumor Using MRI

Ejaz

Suaib²,

Kamal

et al. 2023

IEEE Access

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The features play an important role for identification of the region of interest. Different kind of features exist, it is also essential to identify the accurate class of the features, challenging dataset like MICCAI BraTs brain tumor contains many tumor images. Features are helpful to detect the region of tumor with some of their characteristic. But due to many images and their information, the issue of data complexity is raised. because the data was found to be complex. Thus, due to the complexity, higher dimension features are reduced to low dimension features. Hence, there is a need for improved feature selection method. Furthermore, it is also essential to enhance the method for the SOM Map for the selection of deterministic feature after the extraction. The goal of the work is not only to select the accurate feature of tumor but also to segment the tumor intensity with the confidence element. The objective under umbrella of this work is to improve the feature selection method by using confidence element of interest through the determination of the best feature using the SOFM with FCM. The method works with the selection of the best features with higher accuracy. Those higher accurate Features are called the deterministic Features. These deterministic features are selected through improved weighted SOM. This improved SOM is further combined with FCM to cluster the Confidence element. Evaluation is made with comparison to ground truth reality images; Results show; DOI is 0.94, JI is 0.91, MSE is 0.058db and PSNR is 17.94db; MSE with small number highlights the performance of method. It can be compared with the state of the art or it can be compared with benchmark studies. Testing parameters from benchmark studies were JI, DOI, MSE and PSNR: JI accuracy value was 31.5%, DOI accuracy value was 47.3%, MSE value was 2.5dB and PSNR value was 40dB.A better region of interest is proposed method to determine the confidence element. The average accuracy over the dataset is determined in form of confidence element (ROI), overlap is for complex cases and average value is 94 percent.

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Section: Literature Reviewsmentioning

confidence: 99%

Segmentation Method of Deterministic Feature Clustering for Identification of Brain Tumor Using MRI

Ejaz

Suaib²,

Kamal

et al. 2023

IEEE Access

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“…The filtering operation is done using regular statistical metrics which depict the image texture. This depiction offers information on the local variability of the pixel's intensity values in an image [31]. The calculation of SD is as follows [32].…”

Section: Segmentation Template Generationmentioning

confidence: 99%

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

Ibrahim

Al-Saleh

Jabar

2023

IJEECS

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<p>Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.</p>

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“…The segmentation can be also achieved through good statistical calculation, computational application on the dataset, and confusion matrix with its derivations [ 44 ]. It has been acknowledged that there is a conventional technique of segmentation which removes noise over boundaries of an MR image using filters [ 45 ]. Automatic segmentation becomes challenging if there are a variety of tumor tissues in the image [ 46 ].…”

Section: Related Workmentioning

confidence: 99%

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Ejaz

Rahim

Arif

et al. 2022

Contrast Media & Molecular Imaging

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Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries.

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Medical Image Texture Segmentation Usingrange Filter

Abstract: ABSTRACT

Cited by 4 publications

References 13 publications

Segmentation Method of Deterministic Feature Clustering for Identification of Brain Tumor Using MRI

Segmentation Method of Deterministic Feature Clustering for Identification of Brain Tumor Using MRI

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

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