Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

Joint Authors

Gallegos, F. J.
Mújica Vargas, Dante
Vianney Kinani, Jean Marie
Rosales Silva, Alberto Jorge
Ramos Díaz, Eduardo
Arellano, Alfonso

Source

Journal of Healthcare Engineering

Issue

Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2017-10-12

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Public Health
Medicine

Abstract EN

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy.

We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering.

We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation.

The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy.

Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method.

An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained.

Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets.

As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

American Psychological Association (APA)

Vianney Kinani, Jean Marie& Rosales Silva, Alberto Jorge& Gallegos, F. J.& Mújica Vargas, Dante& Ramos Díaz, Eduardo& Arellano, Alfonso. 2017. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1181256

Modern Language Association (MLA)

Vianney Kinani, Jean Marie…[et al.]. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering. Journal of Healthcare Engineering No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1181256

American Medical Association (AMA)

Vianney Kinani, Jean Marie& Rosales Silva, Alberto Jorge& Gallegos, F. J.& Mújica Vargas, Dante& Ramos Díaz, Eduardo& Arellano, Alfonso. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1181256

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1181256