Probabilistic mutual information based extraction of malignant brain tumors in MR images

Vidyarthi, Ankit; Mittal, Namita

doi:10.1109/iciinfs.2014.7036642

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally the tree clusters are merged on the basis of the minimum distance computation between tree clusters. The approach described in [15] is used as termination criteria for an algorithm while the approach for cluster merging using probabilistic mutual information is proposed in [17]. Merging of the clusters based on the probability distributional value of the tree clusters overcomes the limitations of the distance based metric for finding the similarity.…”

Section: Related Workmentioning

confidence: 99%

Brain tumor segmentation approaches: Review, analysis and anticipated solutions in machine learning

Vidyarthi

Mittal

2015

2015 39th National Systems Conference (NSC)

Self Cite

View full text Add to dashboard Cite

Brain tumor is one of the most rigorous diseases in the medical science. An effective and efficient analysis is always a key concern for the radiologists in the premature phase of tumor growth. At first sight of the imaging modality like in Magnetic Resonance (MR) imaging, the proper visualization of the tumor cells and its differentiation with its nearby soft tissues is somewhat difficult task. The reason for the above problem is the presence of the low illumination in imaging modalities. One of the solutions of such problem is deal by using machine learning based system diagnosis. In past various segmentation methods had been applied on brain MR imaging system to figure out the proper abnormality region from overall volume of the brain. In this paper a decade survey analysis is presented for all such approaches which are used in machine learning system for tumor segmentation. Further, the paper presents the limitations and advantages of all such approaches in machine learning based diagnosis. At last, the comparative segmentation results are discussed with certain clustering performance measures to analyse the effectiveness of each algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Brain tumor segmentation approaches: Review, analysis and anticipated solutions in machine learning

Vidyarthi

Mittal

2015

2015 39th National Systems Conference (NSC)

Self Cite

View full text Add to dashboard Cite

show abstract

A Dual Cascaded Deep Theoretic Learning Approach for the Segmentation of the Brain Tumors in MRI Scans

Sreedhar,

Dara,

Srinivasulu

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and monitoring of patients with neurological disorders. This paper proposes an approach for brain tumor segmentation employing a cascaded architecture integrating L‐Net and W‐Net deep learning models. The proposed cascaded model leverages the strengths of U‐Net as a baseline model to enhance the precision and robustness of the segmentation process. In the proposed framework, the L‐Net excels in capturing the mask, while the W‐Net focuses on fine‐grained features and spatial information to discern complex tumor boundaries. The cascaded configuration allows for a seamless integration of these complementary models, enhancing the overall segmentation performance. To evaluate the proposed approach, extensive experiments were conducted on the datasets of BraTs and SMS Medical College comprising multi‐modal MRI images. The experimental results demonstrate that the cascaded L‐Net and W‐Net model consistently outperforms individual models and other state‐of‐the‐art segmentation methods. The performance metrics such as the Dice Similarity Coefficient value achieved indicate high segmentation accuracy, while Sensitivity and Specificity metrics showcase the model's ability to correctly identify tumor regions and exclude healthy tissues. Moreover, the low Hausdorff Distance values confirm the model's capability to accurately delineate tumor boundaries. In comparison with the existing methods, the proposed cascaded scheme leverages the strengths of each network, leading to superior performance compared to existing works of literature.

show abstract

Probabilistic mutual information based extraction of malignant brain tumors in MR images

Cited by 2 publications

References 19 publications

Brain tumor segmentation approaches: Review, analysis and anticipated solutions in machine learning

Brain tumor segmentation approaches: Review, analysis and anticipated solutions in machine learning

A Dual Cascaded Deep Theoretic Learning Approach for the Segmentation of the Brain Tumors in MRI Scans

Contact Info

Product

Resources

About