Iterative randomized irregular circular algorithm for proliferation rate estimation in brain tumor Ki-67 histology images

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Proliferation rate estimation (PRE) is clinically performed from Ki-67 histopathology images. As brain tumor tissues are very complex, accurate PRE determination requires manual cell counting that is tedious, time consuming and inherently inaccurate due to inter-personal variations. Therefore, pathologists usually determine the PRE based on their experience and visualization without actual counting. Automating PRE can substantially increase the efficiency and accuracy of pathologists' determination of PRE. In addition, developing a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. In this paper, a PRE Computer Aided Diagnosis (PRECAD) system has been developed to automate the determination of PRE from Ki-67 histopathology microscopic images for brain tumors. The process involves six steps: color space transformation, customized color modification, nuclei segmentation based on K-Means clustering, preprocessing the extracted cells, counting based on an iterative structured circle detection (IRIC) algorithm, and finally, calculating the PRE value. The proposed IRIC algorithm is able to detect irregular and overlapping cells by introducing dynamic initialization to the basic RCD method, dividing the entire image into partitions based on 8-neighbor connected components. We initiated a new selection method for determining a best circle candidate that yields a reduced probability of incorrectly detecting circles, and proposed a new technique for detecting irregular cells via a dynamic number of iterations that guarantees finding all the cells in a selected partition. Using the same innovations mentioned above, our proposed IRIC algorithm can also be used to detect irregular and two or more overlapping cells. The proposed PRECAD system achieved high accuracy, as determined by quantitative analysis of precision, recall and F-measurement test values of 97.8%, 98.3% and 98% for blue cells and 98.7%, 98% and 98.4% for brown cells, respectively. Thus, our proposed PRECAD system is as reliable as a pathologist for estimating the proliferation rate, while also featuring inherent reproducibility.

Original languageEnglish
Pages (from-to)111-129
Number of pages19
JournalExpert Systems with Applications
Volume48
DOIs
Publication statusPublished - 15 Apr 2016

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Histology
Tumors
Brain
Computer aided diagnosis
Color
Visualization
Innovation
Tissue
Chemical analysis

Keywords

  • Brain tumor
  • Circle detection
  • Digital histopathology
  • Ki-67
  • Nuclei counting
  • Proliferation rate

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Cite this

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title = "Iterative randomized irregular circular algorithm for proliferation rate estimation in brain tumor Ki-67 histology images",
abstract = "Proliferation rate estimation (PRE) is clinically performed from Ki-67 histopathology images. As brain tumor tissues are very complex, accurate PRE determination requires manual cell counting that is tedious, time consuming and inherently inaccurate due to inter-personal variations. Therefore, pathologists usually determine the PRE based on their experience and visualization without actual counting. Automating PRE can substantially increase the efficiency and accuracy of pathologists' determination of PRE. In addition, developing a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. In this paper, a PRE Computer Aided Diagnosis (PRECAD) system has been developed to automate the determination of PRE from Ki-67 histopathology microscopic images for brain tumors. The process involves six steps: color space transformation, customized color modification, nuclei segmentation based on K-Means clustering, preprocessing the extracted cells, counting based on an iterative structured circle detection (IRIC) algorithm, and finally, calculating the PRE value. The proposed IRIC algorithm is able to detect irregular and overlapping cells by introducing dynamic initialization to the basic RCD method, dividing the entire image into partitions based on 8-neighbor connected components. We initiated a new selection method for determining a best circle candidate that yields a reduced probability of incorrectly detecting circles, and proposed a new technique for detecting irregular cells via a dynamic number of iterations that guarantees finding all the cells in a selected partition. Using the same innovations mentioned above, our proposed IRIC algorithm can also be used to detect irregular and two or more overlapping cells. The proposed PRECAD system achieved high accuracy, as determined by quantitative analysis of precision, recall and F-measurement test values of 97.8{\%}, 98.3{\%} and 98{\%} for blue cells and 98.7{\%}, 98{\%} and 98.4{\%} for brown cells, respectively. Thus, our proposed PRECAD system is as reliable as a pathologist for estimating the proliferation rate, while also featuring inherent reproducibility.",
keywords = "Brain tumor, Circle detection, Digital histopathology, Ki-67, Nuclei counting, Proliferation rate",
author = "Alomari, {Yazan M.} and {Sheikh Abdullah}, {Siti Norul Huda} and {Md Zain}, {Reena Rahayu} and Khairuddin Omar",
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AU - Alomari, Yazan M.

AU - Sheikh Abdullah, Siti Norul Huda

AU - Md Zain, Reena Rahayu

AU - Omar, Khairuddin

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N2 - Proliferation rate estimation (PRE) is clinically performed from Ki-67 histopathology images. As brain tumor tissues are very complex, accurate PRE determination requires manual cell counting that is tedious, time consuming and inherently inaccurate due to inter-personal variations. Therefore, pathologists usually determine the PRE based on their experience and visualization without actual counting. Automating PRE can substantially increase the efficiency and accuracy of pathologists' determination of PRE. In addition, developing a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. In this paper, a PRE Computer Aided Diagnosis (PRECAD) system has been developed to automate the determination of PRE from Ki-67 histopathology microscopic images for brain tumors. The process involves six steps: color space transformation, customized color modification, nuclei segmentation based on K-Means clustering, preprocessing the extracted cells, counting based on an iterative structured circle detection (IRIC) algorithm, and finally, calculating the PRE value. The proposed IRIC algorithm is able to detect irregular and overlapping cells by introducing dynamic initialization to the basic RCD method, dividing the entire image into partitions based on 8-neighbor connected components. We initiated a new selection method for determining a best circle candidate that yields a reduced probability of incorrectly detecting circles, and proposed a new technique for detecting irregular cells via a dynamic number of iterations that guarantees finding all the cells in a selected partition. Using the same innovations mentioned above, our proposed IRIC algorithm can also be used to detect irregular and two or more overlapping cells. The proposed PRECAD system achieved high accuracy, as determined by quantitative analysis of precision, recall and F-measurement test values of 97.8%, 98.3% and 98% for blue cells and 98.7%, 98% and 98.4% for brown cells, respectively. Thus, our proposed PRECAD system is as reliable as a pathologist for estimating the proliferation rate, while also featuring inherent reproducibility.

AB - Proliferation rate estimation (PRE) is clinically performed from Ki-67 histopathology images. As brain tumor tissues are very complex, accurate PRE determination requires manual cell counting that is tedious, time consuming and inherently inaccurate due to inter-personal variations. Therefore, pathologists usually determine the PRE based on their experience and visualization without actual counting. Automating PRE can substantially increase the efficiency and accuracy of pathologists' determination of PRE. In addition, developing a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. In this paper, a PRE Computer Aided Diagnosis (PRECAD) system has been developed to automate the determination of PRE from Ki-67 histopathology microscopic images for brain tumors. The process involves six steps: color space transformation, customized color modification, nuclei segmentation based on K-Means clustering, preprocessing the extracted cells, counting based on an iterative structured circle detection (IRIC) algorithm, and finally, calculating the PRE value. The proposed IRIC algorithm is able to detect irregular and overlapping cells by introducing dynamic initialization to the basic RCD method, dividing the entire image into partitions based on 8-neighbor connected components. We initiated a new selection method for determining a best circle candidate that yields a reduced probability of incorrectly detecting circles, and proposed a new technique for detecting irregular cells via a dynamic number of iterations that guarantees finding all the cells in a selected partition. Using the same innovations mentioned above, our proposed IRIC algorithm can also be used to detect irregular and two or more overlapping cells. The proposed PRECAD system achieved high accuracy, as determined by quantitative analysis of precision, recall and F-measurement test values of 97.8%, 98.3% and 98% for blue cells and 98.7%, 98% and 98.4% for brown cells, respectively. Thus, our proposed PRECAD system is as reliable as a pathologist for estimating the proliferation rate, while also featuring inherent reproducibility.

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KW - Nuclei counting

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