Microscopic images classification for cancer diagnosis

被引：0
作者：
Yashwant Kurmi
Vijayshri Chaurasia
Narayanan Ganesh
Abhimanyu Kesharwani
机构：
[1] Maulana Azad National Institute of Technology,
[2] Jawaharlal Nehru Cancer Hospital and Research Center,undefined
[3] All India Institute of Medical Sciences Bhopal,undefined
来源：
Signal, Image and Video Processing | 2020年 / 14卷
关键词：
Medical imaging; Histopathology; Histopathology image; Feature extraction; Image classification;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Computer aided diagnosis of cancer is a field of substantial worth in current scenario since approximately 38% population of the world is suffering from the disease. The detection of cancer is based on the observation of deformation in nuclei structure using histopathology slides/images. The proposed technique utilizes nuclei localization prior to classification of histopathology images as benign and malignant. The features used for classification are an ensemble of 150 bag of visual word features, extracted from preprocessed image and 20 handcrafted features, extracted from the internal parts of nuclei using localized histopathology images. The simulation results confirm the superiority of proposed localization based cancer classification method as compared to existing methods of the domain. It has reported average classification accuracy of 95.03% on BreakHis dataset.
引用
收藏
页码：665 / 673
页数：8
相关论文
共 63 条
  • [1] Jung C(2010)Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization IEEE Trans. Biomed. Eng. 57 2600-2604
  • [2] Kim C(2016)Automatic cell nuclei segmentation and classification of breast cancer histopathology images Signal Process. 122 1-13
  • [3] Wang P(2016)A semi-automatic method for robust and efficient identification of neighboring muscle cells Pattern Recogn. 53 300-312
  • [4] Hu X(2016)Image contrast enhancement using weighted transformation function IEEE Sens. J. 16 7534-7536
  • [5] Li Y(2014)Prostate cancer grading: use of graph cut and spatial arrangement of nuclei IEEE Trans. Med. Imag 33 2254-2270
  • [6] Liu Q(2017)Evaluation of three algorithms for the segmentation of overlapping cervical cells IEEE J. Biomed. Health Inf. 21 441-450
  • [7] Zhu X(2016)A dataset for breast cancer histopathological image classification IEEE Trans. Biomed. Eng. 63 1455-1462
  • [8] Wang Z(2016)Histopathological image classification using discriminative feature-oriented dictionary learning IEEE Trans. Med Imag 35 738-751
  • [9] Jabeen A(2018)Segmentation of nuclei in histopathology images by deep regression of the distance map IEEE Trans. Med. Imag 21 150-161
  • [10] Riaz MM(2017)Circular mixture modeling of color distribution for blind stain separation in pathology images IEEE J. Biomed. Health Inf. 30 621-631
1234567