Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review.
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determ...
| Published in: | Journal of Medical Systems Vol. 46; no. 1; pp. 1 - 25 |
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| Main Authors: | , , |
| Format: | pictorial review tables/charts Journal Article |
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Springer Nature
Jan2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154427436&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154427436 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2022 vid: 46 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154427436 154427436 154427436 10.1007/s10916-021-01786-9 154427436 ppf: 1 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review. aug: au: Rashmi, R Prasad, Keerthana Udupa, Chethana Babu K affil: Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India sug: subj: Histological Techniques Breast Neoplasms Pathology Image Processing, Computer Assisted Breast Neoplasms Diagnosis Machine Learning Deep Learning Staining and Labeling Microscopy Autoanalysis Image Interpretation, Computer Assisted Diagnosis, Computer Assisted Decision Support Systems, Clinical ab: Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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