Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images

Ryujiro YOKOYAMA; Xuejun ZHANG; Yoshikazu UCHIYAMA; Hiroshi FUJITA; Takeshi HARA; Xiangrong ZHOU; Masayuki KANEMATSU; Takahiko ASANO; Hiroshi KONDO; Satoshi GOSHIMA; Hiroaki HOSHI; Toru IWAMA

doi:10.1093/ietisy/e90-d.6.943

Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images

Ryujiro YOKOYAMA, Xuejun ZHANG, Yoshikazu UCHIYAMA, Hiroshi FUJITA, Takeshi HARA, Xiangrong ZHOU, Masayuki KANEMATSU, Takahiko ASANO, Hiroshi KONDO, Satoshi GOSHIMA, Hiroaki HOSHI, Toru IWAMA

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The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: "isolated lacunar infarct regions" and "lacunar infarct regions adjacent to hyperintensive structures." The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -- area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.

Publication: IEICE TRANSACTIONS on Information Vol.E90-D No.6 pp.943-954

Publication Date: 2007/06/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e90-d.6.943

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

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Ryujiro YOKOYAMA, Xuejun ZHANG, Yoshikazu UCHIYAMA, Hiroshi FUJITA, Takeshi HARA, Xiangrong ZHOU, Masayuki KANEMATSU, Takahiko ASANO, Hiroshi KONDO, Satoshi GOSHIMA, Hiroaki HOSHI, Toru IWAMA, "Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images" in IEICE TRANSACTIONS on Information, vol. E90-D, no. 6, pp. 943-954, June 2007, doi: 10.1093/ietisy/e90-d.6.943.
Abstract: The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: "isolated lacunar infarct regions" and "lacunar infarct regions adjacent to hyperintensive structures." The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -- area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.6.943/_p

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@ARTICLE{e90-d_6_943,
author={Ryujiro YOKOYAMA, Xuejun ZHANG, Yoshikazu UCHIYAMA, Hiroshi FUJITA, Takeshi HARA, Xiangrong ZHOU, Masayuki KANEMATSU, Takahiko ASANO, Hiroshi KONDO, Satoshi GOSHIMA, Hiroaki HOSHI, Toru IWAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images},
year={2007},
volume={E90-D},
number={6},
pages={943-954},
abstract={The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: "isolated lacunar infarct regions" and "lacunar infarct regions adjacent to hyperintensive structures." The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -- area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.},
keywords={},
doi={10.1093/ietisy/e90-d.6.943},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images
T2 - IEICE TRANSACTIONS on Information
SP - 943
EP - 954
AU - Ryujiro YOKOYAMA
AU - Xuejun ZHANG
AU - Yoshikazu UCHIYAMA
AU - Hiroshi FUJITA
AU - Takeshi HARA
AU - Xiangrong ZHOU
AU - Masayuki KANEMATSU
AU - Takahiko ASANO
AU - Hiroshi KONDO
AU - Satoshi GOSHIMA
AU - Hiroaki HOSHI
AU - Toru IWAMA
PY - 2007
DO - 10.1093/ietisy/e90-d.6.943
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2007
AB - The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: "isolated lacunar infarct regions" and "lacunar infarct regions adjacent to hyperintensive structures." The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -- area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.
ER -