EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images
- Title
- EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images
- Creator
- Mukku L.; Thomas J.
- Description
- Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance.Before segmenting the cervical region, specular reflection removal is an efficient one. Because, cervical cancer can be found using a visual check with acetic acid, which turns precancerous and cancerous areas whiteand these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-whiteareas and should therefore be removed. So, in this paper, specular reflection removal with segmentingthe cervix region ina colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican Axolotl Optimization (AMAO) algorithm. The performance of the proposed approach is analyzed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
- Source
- Multimedia Tools and Applications
- Date
- 2024-01-01
- Publisher
- Springer
- Subject
- Cervical region; Colposcopy; Enhanced Gaussian mixture model; Mexican axolotl optimization; Specular reflection
- Coverage
- Mukku L., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India; Thomas J., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 13807501; CODEN: MTAPF
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Mukku L.; Thomas J., “EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/13703.