Book chapter
An Integrated Shape-Texture Descriptor for Modeling Whole-Organism Phenotypes in Drug Screening
Advances in Visual Computing: 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16–18, 2023, Proceedings, Part I, pp.394-405
Lecture Notes in Computer Science, v. 14361, Springer Nature Switzerland
2023
DOI: 10.1007/978-3-031-47969-4_31
Abstract
Schistosomiasis is a parasitic disease with global health and socio-economic impacts. The World Health Organization (WHO) and National Institutes of Health (NIH) list it among diseases for which new treatments are urgently required. Drug discovery for Schistosomiasis typically involves whole-organism phenotypic screening. In such an approach, the parasites are exposed to different chemical compounds, and systemic phenotypic effects captured via microscopy (video or still images) are analyzed to identify promising molecules. Changes in parasite phenotypes tend to be multidimensional, involving changes in shape, appearance and behavior, and time-varying. In many image representation frameworks, shape and appearance are measured independently and their inter-correlation can be lost. In this paper, we propose an integrated shape-texture descriptor called the skeleton-constrained shortest band (SCSB) that extends the family of shape context descriptors well known in computer vision. We examine how SCSB can be used to measure temporally varying shape and appearance changes occurring as a consequence of chemical action and compare its performance with other members of the shape context family.
Details
- Title: Subtitle
- An Integrated Shape-Texture Descriptor for Modeling Whole-Organism Phenotypes in Drug Screening
- Creators
- Jiadong YuRahul Singh - University of Iowa
- Resource Type
- Book chapter
- Publication Details
- Advances in Visual Computing: 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16–18, 2023, Proceedings, Part I, pp.394-405
- Publisher
- Springer Nature Switzerland; Cham
- Series
- Lecture Notes in Computer Science; v. 14361
- DOI
- 10.1007/978-3-031-47969-4_31
- ISSN
- 1611-3349
- Language
- English
- Date published
- 2023
- Academic Unit
- Computer Science
- Record Identifier
- 9984539651602771
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