Corpus ID: 8952443

Automated cancer diagnosis based on histopathological images : a systematic survey

@inproceedings{Demir2005AutomatedCD,
  title={Automated cancer diagnosis based on histopathological images : a systematic survey},
  author={Cigdem Demir and B{\"u}lent Yener},
  year={2005}
}
In traditional cancer diagnosis, pathologists examine biopsies to make diagnostic assessments largely based on cell morphology and tissue distribution. However, this is subjective and often leads to considerable variability. On the other hand, computational diagnostic tools enable objective judgments by making use of quantitative measures. This paper presents a systematic survey of the computational steps in automated cancer diagnosis based on histopathology. These computational steps are: 1… Expand

Figures from this paper

A survey on automated cancer diagnosis from histopathology images
TLDR
This survey, explores the state-of-the-art materials and methods that have been used for CAD to detect cancer from histopathology images. Expand
Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential
TLDR
This study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. Expand
HISTOPATHOLOGICAL IMAGE ANALYSIS USING IMAGE PROCESSING TECHNIQUES : AN OVERVIEW
TLDR
This paper reviews and summarizes the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods. Expand
Prominent Features in Identifying Fibrosis in Microscopic Tissue Images
TLDR
A brief review of prominent features used in identifying fibrosis in microscopic tissue images and the usage of automated computerized diagnosis using digital image processing for the recognition of fibrosis is provided. Expand
Automated Mitosis Detection in Color and Multi-spectral High-Content Images in Histopathology: Application to Breast Cancer Grading in Digital Pathology
TLDR
The main goal of this research is the development of frameworks able to provide detection of mitosis on different types of scanners and multispectral microscope and compared results with ICPR MITOS contest 2012. Expand
A Survey on Computer Vision Based Diagnosis for Skin Lesion Detection
TLDR
The process of skin lesion detection is shown and some clinical diagnosis methods which are being incorporated with the tool for detecting the type of lesion are discussed. Expand
Learning histopathological patterns
TLDR
The aim was to demonstrate a method for automated image analysis of immunohistochemically stained tissue samples for extracting features that correlate with patient disease and obtain good performance compared to state-of-the-art nuclei separation. Expand
Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images
TLDR
A fast and accurate approach for automatic mitosis detection from histopathological images is proposed by restricting the scales with the maximization of relative-entropy between the cells and the background to result in precise cell segmentation. Expand
Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends
TLDR
In this paper, the challenges of histopathology image analysis are evaluated and an extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. Expand
Image Analysis of Glioblastoma Histopathology
TLDR
The work presents a two-step process of iterative thresholding and cleaving (ITC) to identify aforementioned structures and ensures that the identification of regions important for the diagnosis process is distinctly clearer using the ITC approach than with standard approaches such as the Otsu method and adaptive thresholding. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 80 REFERENCES
Automated breast tumor diagnosis and grading based on wavelet chromatin texture description.
TLDR
Wavelets were employed for multi-scale image analysis to extract parameters for the description of chromatin texture in the cytological diagnosis and grading of invasive breast cancer and show that wavelets perform excellently with classification scores comparable with densitometric and co-occurrence features. Expand
AUTOMATED LOCATION OF DYSPLASTIC FIELDS IN COLORECTAL HISTOLOGY USING IMAGE TEXTURE ANALYSIS
TLDR
It is demonstrated that abnormalities in low‐power tissue morphology can be identified using quantitative image analysis, and the identification of diagnostically useful fields advances the potential of automated systems in histopathology. Expand
The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia.
TLDR
The use of imaging for identifying tissue abnormalities in prostate histology is explored and the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision is illustrated. Expand
Computer-aided classification of breast cancer nuclei.
TLDR
By using a nuclear classification module such as the one introduced in this paper it is possible to translate low-level system measurements into a vocabulary that is familiar to medical experts by improving the accuracy in grading breast cancer nuclei. Expand
Computer-aided diagnosis of mammographic microcalcification clusters.
TLDR
The development and evaluation of a computer-aided detection and diagnosis algorithm for mammographic calcification clusters that could exceed the performance of a similar visual analysis system that was used as basis for development and could be applied to images from different imaging systems and film digitizers with similar sensitivity and specificity rates. Expand
Image analysis and morphometry in the diagnosis of breast cancer
TLDR
The present review summarizes the main problems and the general approach to the use of this technique for quantitating immunohistochemical stain results, obtaining DNA histograms, and making de novo diagnoses in routine materials of the Pathology service. Expand
Nuclear feature extraction for breast tumor diagnosis
TLDR
Interactive image processing techniques, along with a linear-programming-based inductive classifier, have been used to create a highly accurate system for diagnosis of breast tumors, resulting in an accuracy of 86% and an improvement over the best diagnostic results in the medical literature. Expand
Computer-derived nuclear features distinguish malignant from benign breast cytology.
TLDR
The use of computer-based analytical techniques to define nuclear size, shape, and texture features are used to distinguish between benign and malignant breast cytology, and nine benign aspirates fell into the suspicious category. Expand
Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas.
TLDR
The proposed image analysis system may be of value to the objective assessment of the malignancy of urine bladder carcinomas, since it relies on nuclear parameters that are employed in visual grading and their prognostic value has been proved. Expand
Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa
TLDR
The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported and it is shown that correlation and entropy were the features that discriminated most strongly between normal andcancerous tissue. Expand
...
1
2
3
4
5
...