Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

Ziba Gandomkar; Patrick C Brennan; Claudia Mello-Thoms

doi:10.4103/jpi.jpi_22_17

Back

Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

Journal article

Open access

Peer reviewed

Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

Ziba Gandomkar, Patrick C Brennan and Claudia Mello-Thoms

Journal of pathology informatics, Vol.8(1), pp.34-34

2017

DOI: 10.4103/jpi.jpi_22_17

PMCID: PMC5609395

PMID: 28966834

Files and links (1)

url

https://doi.org/10.4103/jpi.jpi_22_17View

Published (Version of record) Open Access

Abstract

Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as "probably a mitosis" by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using -means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. The Kruskal-Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects.

breast histopathology

textural features

Breast cancer

intensity-based features

mitotic figures

Details

Title: Subtitle: Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
Creators: Ziba Gandomkar - Medical Image Optimisation and Perception Research Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Australia
Patrick C Brennan - Medical Image Optimisation and Perception Research Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Australia
Claudia Mello-Thoms - Department of Biomedical Informatics, University of Pittsburgh School of Medicine, USA
Resource Type: Journal article
Publication Details: Journal of pathology informatics, Vol.8(1), pp.34-34
DOI: 10.4103/jpi.jpi_22_17
PMID: 28966834
PMCID: PMC5609395
NLM abbreviation: J Pathol Inform
ISSN: 2229-5089
eISSN: 2153-3539
Publisher: India
Language: English
Date published: 2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology
Record Identifier: 9984051796102771

Metrics

30 Record Views

See more details