A novel triple-level combinational framework for brain anomaly segmentation to augment clinical diagnosis

Suaib²,

Kamal

et al. 2023

IEEE Access

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.

Section: Literature Reviewsmentioning

confidence: 99%

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

Suaib²,

Kamal

et al. 2023

IEEE Access

“…In this referenced study, better tumour detection is found with flavour of SVM classifier for Harvard brain tumour image [27]. In another referenced study, SOM pixel labelling with reduced cluster and deterministic feature clustering give better segmentation [28][29][30][31]. Medical imaging is an important field for identification of disease, so doctor can take better decision in perspective of intelligent computation [32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Confidence Region Identification and Contour Detection in MRI Image

Computational Intelligence and Neuroscience

Arif

Rahim³

et al. 2022

Self Cite

Tumour region extraction (RE) method identifies the area of interest in MR imaging as it also highlights tumour boundaries. Some other intensities are existing, they are not visible but have their existence in region, and this region is called growing region. Such region is to be tumour region. Due to the variation of intensities in MRI images, tumour visibility becomes uncleared. Tumour intensity variations (tumour tissues) mix with normal brain tissues. In the light of above circumstance, tumour growing region becomes challenge. The goal of work is to extract the region of interest with confidence. The objective of the study is to develop the region of interest of brain tumour MRI image method by using confidence score for identifying the variation of intensity. The significance of work is based on identification of region of interest (tumour region). Confidence score is measured through pattern of intensities of MRI image. Similar patterns of brain tumour intensities are identified. Each pattern of intensities is adjusted with certain scale, and then biggest blob is analysed. Various biggest area blobs are combined, and resultant biggest blob is formed. In fact, resultant area blob is a combination of different patterns. Each pattern is assigned with particular colour. These colours highlight the growing region. Further, a contour is detected around the tumour boundaries. With combination of region scale fitting and contour detection (CD), tumour boundaries are further separated from normal tissues. Hence, the confidence score (CS) is formed from CD. CS is further converted to confidence region (CR). Conversion to CR is performed though confidence interval (CI). CI is based on defined conditions. In such conditions, different probabilities are considered. Probability identifies the region. Source of region formation is pixels; these pixels highlight tumour core significantly. This CR is obtained through checking standard deviation and statistical evaluation using confidence interval. Hence, region-of-interest pixels are identifying the CR. CR is evaluated through 97% Dice over index (DOI), 94% Jacquard, MSE 1.24, and PSNR 17.45. Value of testing parameter from benchmark study was JI, DOI, and MSE, PSNR : JI was 31.5%, DOI was 47.3%, MSE was 2.5 dB, and PSNR was 40 dB. The parameters are measured for the complex images; contribution parameter classifies the mean pixel values and deviating pixel values, and the classification of the pixel value is like to be termed as intensities. Mentioned classification extracts the variation of intensity pixels accurately; then, algorithm is highlighting the region as compared to the normal tumour cells.

“…In [77], K-means is combined with CNN; in parallel for segmentation, the accuracy with unsupervised learning is more accurate as compared to supervised learning and CNN. In [78][79][80][81][82][83][84], segmentation methods are compared with deep learning. Hence, it is emphasized that not a single study among the referenced ones was capable of solving the issues of variation of intensities, extreme intensities, and features clustering.…”

Section: Related Workmentioning

confidence: 99%

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

Contrast Media & Molecular Imaging

Rahim

Arif

et al. 2022

Self Cite

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.