Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

Abstract

Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element.. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

Light microscopic iris classification using ensemble multi‐class support vector machine

“…Preprocessing is normally performed in almost all sorts of image analysis and classification to remove the unnecessary detail and enhance the input image quality for better performance (Adeel et al, 2020; Al‐Ameen et al, 2015; Amin, Sharif, Raza, Saba, & Anjum, 2019; Amin, Sharif, Raza, Saba, & Rehman, 2019; Amin, Sharif, Raza, Saba, Sial, & Shad, 2019; Amin, Sharif, Rehman, Raza, & Mufti, 2018; Amin, Sharif, Yasmin, Saba, & Raza, 2019; Ejaz et al, 2018; Ejaz et al, 2020; Iqbal et al, 2019; Iqbal, Ghani, Saba, & Rehman, 2018; Iqbal, Khan, Saba, & Rehman, 2017; Javed, Rahim, & Saba, 2019; Javed, Rahim, Saba, & Rashid, 2019; Javed, Rahim, Saba, & Rehman, 2020; Javed, Saba, Shafry, & Rahim, 2020; Saba, 2020; Saba, Bokhari, et al, 2018). Accordingly, in the proposed approach, image intensities are normalized using histogram equalization (Ali et al, 2016) and Gaussian un‐sharp mask is applied to enhanced its quality.…”

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

“…With VGG‐19 (CNN architecture) 98.78, 99.63, and 99.67% accuracy reported on (BraTS) 2015, 2016, and 2017, respectively. Ejaz et al (2020) proposed hybrid SOM pixel labeling with reduce cluster membership and deterministic feature clustering for brain tumor identification using MICCAI BraTS dataset. To segment brain tumor, cluster obtained using three unsupervised learning techniques.…”

Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

“…These kinds of unwanted details or artifacts decrease the quality of the images and decrease the chances of accurate skin lesion detection due to its high similarity between surrounding skin and lesion. Therefore, a preprocessing step is introduced for image quality enhancement by removing unwanted details of noise from the images such as bubbles, hairs etc., if the preprocessing steps are not performed appropriately, there are high chances of inaccurate segmentation and recognition of cancer (Ejaz, Rahim, Rehman, Chaudhry, et al, 2018;Ejaz et al, 2020;Ejaz, Rahim, Bajwa, Rana, & Rehman, 2019;.…”