OBJECTIVE: To develop a computer-aided robust nuclei segmentation technique for quantitative histopathological image analysis. STUDY DESIGN: A robust nuclei segmentation technique for histopathological image analysis is proposed. The proposed technique uses a hybrid morphological reconstruction module to reduce the intensity variation within the nuclei regions and suppress the noise in the image. A local region adaptive threshold selection module based on local optimal threshold is used to segment the nuclei. The technique incorporates domain-specific knowledge of skin histopathological images for more accurate segmentation results. RESULTS: The technique is compared to the manually labeled nuclei locations and nuclei boundaries for the performance evaluations. On different histopathological images of skin epidermis with complex background, containing more than 3000 nuclei, the technique provides a good nuclei detection performance: 88.11% sensitivity rate, 80.02% positive prediction rate and only 5.34% under-segmentation rate compared to the manually labeled nuclei locations. Compared to the 110 manually segmented nuclei regions, the proposed technique provides a good segmentation performance (in terms of the nucleus area, perimeter, and form factor). CONCLUSION: The proposed technique is able to provide more accurate segmentation performance compared to the existing techniques and can be employed for quantitative analysis of the histopathological images.
OBJECTIVE: To develop a computer-aided robust nuclei segmentation technique for quantitative histopathological image analysis. STUDY DESIGN: A robust nuclei segmentation technique for histopathological image analysis is proposed. The proposed technique uses a hybrid morphological reconstruction module to reduce the intensity variation within the nuclei regions and suppress the noise in the image. A local region adaptive threshold selection module based on local optimal threshold is used to segment the nuclei. The technique incorporates domain-specific knowledge of skin histopathological images for more accurate segmentation results. RESULTS: The technique is compared to the manually labeled nuclei locations and nuclei boundaries for the performance evaluations. On different histopathological images of skin epidermis with complex background, containing more than 3000 nuclei, the technique provides a good nuclei detection performance: 88.11% sensitivity rate, 80.02% positive prediction rate and only 5.34% under-segmentation rate compared to the manually labeled nuclei locations. Compared to the 110 manually segmented nuclei regions, the proposed technique provides a good segmentation performance (in terms of the nucleus area, perimeter, and form factor). CONCLUSION: The proposed technique is able to provide more accurate segmentation performance compared to the existing techniques and can be employed for quantitative analysis of the histopathological images.
Authors: Cheng Lu; James S Lewis; William D Dupont; W Dale Plummer; Andrew Janowczyk; Anant Madabhushi Journal: Mod Pathol Date: 2017-08-04 Impact factor: 7.842
Authors: Sudhir Sornapudi; Ronald Joe Stanley; William V Stoecker; Haidar Almubarak; Rodney Long; Sameer Antani; George Thoma; Rosemary Zuna; Shelliane R Frazier Journal: J Pathol Inform Date: 2018-03-05