Medical Ultrasound Image Segmentation Using U-Net Architecture

Title

Medical Ultrasound Image Segmentation Using U-Net Architecture

Creator

Shereena V.B.; Raju G.

Description

This research article discusses the implementation aspects of a Deep Learning architecture based on U-Net for medical image segmentation. A base model of the U-Net architecture is extended and experimented. Unlike the existing model, the input images are enhanced by applying a Non-Local Means filter optimized using a metaheuristic Grey wolf optimization method. Further, the model parameters are modified to achieve better performance. Tests were performed using two benchmark B-mode Ultrasound image datasets of 200 Breast lesion images and 504 Skeletal images. Experimental results demonstrate that the modifications resulted in more accurate segmentation. The performance of the modified implementation is compared with the base model and a Bidirectional Convolutional LSTM architecture. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

Communications in Computer and Information Science, Vol-1613 CCIS, pp. 361-372.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Deep learning; Grey Wolf Optimizer; Non-Local Means; Segmentation; U-Net; Ultrasound

Coverage

Shereena V.B., Department of Computer Applications, MES College, Marampally, Kochi, India; Raju G., SMIEEE, Department of Computer Science and Engineering, Faculty of Engineering, Christ (Deemed to Be University), Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 18650929; ISBN: 978-303112637-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-12638-3_30

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135962927&doi=10.1007%2f978-3-031-12638-3_30&partnerID=40&md5=f3c0ace95232a586cc009cfe84a8655e