Aymen Mouelhi1, Hana Rmili2, Jaouher Ben Ali3, Mounir Sayadi4, Raoudha Doghri5, Karima Mrad6. 1. University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia. Electronic address: aymen_mouelhi@yahoo.fr. 2. University of Tunis El-Manar, ISTMT, Laboratory of Biophysics and Medical Technologies, Tunisia. Electronic address: rmilihana@gmail.com. 3. University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia; FEMTO-ST Institute, AS2M department, UMR CNRS 6174 - UFC / ENSMM /UTBM, Besançon 25000, France. Electronic address: benalijaouher@yahoo.fr. 4. University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia. Electronic address: mounir.sayadi@esstt.rnu.tn. 5. Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006, Tunisia. Electronic address: raoudha.doghri@gmail.com. 6. Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006, Tunisia. Electronic address: Karima.mrad@rns.tn.
Abstract
BACKGROUND AND OBJECTIVE: This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process. METHODS: The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring. RESULTS: Experimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods. CONCLUSIONS: The proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.
BACKGROUND AND OBJECTIVE: This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process. METHODS: The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring. RESULTS: Experimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods. CONCLUSIONS: The proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.