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1. Segmentation and classification of breast cancer
histopathology images
Presented by Under the guidance of
I. Sofiya Dr. D. Murugan
Research Scholar Professor and Head
Dept. of Computer Science and Engg
MS University
2. OBJECTIVE
The main objective of the work is
• To segment the nuclei from breast cancer histopathology
images.
• To classify the breast cancer whether it is benign or malignant.
3. ABSTRACT
• Breast cancer is the most frequently occurring cancer in women worldwide,
hence early diagnosis of malignancy of cancer is very important .
• Breast cancer detection from digitized breast histopathology images and its
grade assessment is crucial task for pathologists. An automated and robust
method of detecting malignancy in breast cancer is required to assist
pathologist.
• First the nucleus are segmented and then the features are extracted for
classification.
4. INTRODUCTION
• Digital Image Processing means processing digital image by means of a
digital computer.
• Its applications range from medicine to entertainment, passing by
geological processing and remote sensing.
• The analysis of histopathological image is done manually by the
pathologist to detect disease which leads to subjective diagnosis of sample
and varies with level of expertise of examiner and it is also time
consuming. Hence an automated diagnosis is required for a trust worthy
result.
5. INTRODUCTION TO
HISTOPATHOLOGY IMAGES
• A biopsy is the removal of a small amount of tissue.
• After biopsy the specimen is preserved using preservative mostly formalin,
to retain it shape.
• Then the water content is removed from the sample and placed in paraffin
wax for hardening the specimen to make thin sections.
• The thin sections are made with microtone and placed in a glass slide. Then
the pathologist uses Hematoxylin and eosin dye to stain parts of the cell.
6. CONTD.,
• Then they are placed under microscope for the histopathologist to look
after the cells and tissue and to diagnose the disease.
• These are digitised as histopathology images by scanning using microscope
slide scanners which are of high resolution.
• The computerized histopathological slides have to undergo some analysis
for an effective and consistent cancer forecasting and diagnosis.
7. SEGMENTATION IN BREAST
CANCER HISTOPATHOLOGY
IMAGES
• One of the most critical steps is the segmentation of the nuclei in the breast
histopathology images because it is very important to study the features of
nuclei to determine the malignancy.
• Due to the high variability of the tissue appearance, reliable cell nuclei
segmentation of BCH images is still a challenging task.
8. CLASSIFICATION OF BREAST
CANCER HISTOPATHOLOGY
IMAGES
• After nuclei are precisely segmented, classification phase should be
implemented.
• The most important aspects of the classification performance are the
features extraction and the classification algorithms.
• Most of the extracted features are morphology-based (area, perimeter, etc)
and texture-based features (contrast, sum of squares, variance, etc)
10. LITERATURE SURVEY
S.
NO
TITLE OF THE PAPER DESCRIPTION YEAR
1 A resolution adaptive deep
hierarchical (RADHicaL)
learning scheme applied to
nuclear segmentation of
digital pathology images
Resolution adaptive deep
hierarchical (RADHicaL)
learning scheme based deep
learning network.
2017
2 Multi-tissue and multi-scale
approach for nuclei
segmentation in H&E
stained images
Multi-tissue and Multi-scale
based Deep learning model.
2018
11. CONTD.,
S.
NO
TITLE OF THE PAPER DESCRIPTION YEAR
3 SAMS-NET: Stain- aware multi-
scale network for instance-based
nuclei segmentation in histology
images
Stain-Aware Multi-Scale
(SAMS-NET) based deep
learning model
2018
4 A Deep Learning Algorithm for
One-step Contour Aware Nuclei
Segmentation of Histopathological
Images
Deep Learning Algorithm
based on One-step Contour
Aware segmentation
2018
5 Accurate segmentation of nuclei in
pathological images via sparse
reconstruction and deep
convolutional networks
sparse reconstruction and
deep convolutional networks
cascaded by multi-layer
convolution networks
2017
12. PERFORMANCE EVALUATION OF
SEGMENTATION ALGORITHMS
• Performance evaluation of following segmentation algorithms are carried
out
OTSU
Mean Shift
K means
FCM
Fast FCM
Fast and Robust FCM
13. OTSU ALGORITHM
1. Read the input image.
2. Compute histogram and probability of each intensity values.
3. Separate into 2 classes by a threshold .
4. Compute weight and mean of two classes
5. Find the variance
6. If the intensity value is lesser than variance it is 0 and if it is greater it is 1.
14. MEAN SHIFT ALGORITHM
1. Read the input image.
2. Randomly select any pixel as center point.
3. Set bandwidth
4. Make a cluster of the center point with the specified bandwidth.
5. Find the mean value of the cluster and make it new cluster point.
6. Make a cluster for the new center point with the same bandwidth.
7. Repeat step (5) and (6) until all the points are visited.
15. K MEANS ALGORITHM
1. Read the input image.
2. Set the number of cluster .
3. Randomly select cluster centers.
4. Calculate the distance between each pixel value and cluster center.
5. Assign data point to the cluster center whose distance from the cluster
center is minimum of all other cluster center.
6. Find the average of data points in the clusters and the average values are
the new cluster center.
7. Go to step (5).
8. Repeat steps 6 and 7 until no new cluster centers are formed.
16. FCM ALGORITHM
1. Read the input image.
2. Set the number of cluster as .
3. Set the fuzziness exponent .
4. Randomly select cluster centers.
5. Calculate membership matrix.
6. Compute new centers
7. Repeat step (5) and (6) until no new centers are formed.
17. FAST FCM ALGORITHM
1. Read the input image.
2. Set the number of cluster as .
3. Set the fuzziness exponent .
4. Randomly select cluster centers.
5. Calculate membership matrix.
6. Find the new centroid .
7. Repeat step (5) and (6) until no new centers are formed.
18. FAST AND ROBUST FCM
ALGORITHM
1. Read the input image.
2. Compute the median filtered image.
3. Set the number of cluster .
4. Update the partition matrix.
5. Modify the partition matrix.
6. Update the cluster centers.
7. Repeat steps 4-6 until no new cluster is formed.
19. DATASET DESCRIPTION
• To compare the performance of segmentation algorithms this work has
been tested over set of breast cancer histopathology images collected from
publicly available database which is called TNBC (Triple Negative Breast
Cancer) dataset.
• The dataset contains 50 digitised breast cancer histopathology slides
images obtained from 11 patients with its respective ground truth images
each of size 512x512 pixels.
20. PERFORMANCE ANALYSIS
• Performance of segmentation algorithms are evaluated using metrics such
as accuracy, sensitivity, specificity, F measure, Positive and negative
prediction value.
Accuracy is the most intuitive performance measure and it is simply a ratio
of correctly predicted observation to the total observations.
Precision is the ratio of correctly predicted positive observations to the
total predicted positive observations.
21. Sensitivity is the ratio of correctly predicted positive observations to the all
observations in actual class.
Specificity is the measures the proportion of actual negatives that are
correctly identified as such.
F Measure is the weighted average of Precision and Recall. F1 score is
more helpful than accuracy in uneven distribution.
22. The positive and negative predictive values (PPV and NPV respectively)
are the proportions of positive and negative results
in statistics and diagnostic tests that are true positive and true
negative results, respectively.
29. PERFORMANCE EVALUATION OF
SEGMENTATION ALGORITHMS
0
20
40
60
80
100
120
Acc Sen spec ppv Npv Fm
FastFCM
FCM
FRFCM
Kmeans
Meanshift
Otsu
Percentage
Metrics
30. CONCLUSION
Many algorithms and methods are developed for segmentation to segment
the nuclei from the breast histopathology images.
Six existing and standard segmentation algorithms are carried out on breast
cancer histopathology images and their performance are evaluated.
Fast and Robust FCM has given best accuracy among the six .
In future, an efficient segmentation algorithm may be proposed to
automatically segment the nuclei.
31. REFERENCES
• Al-Milaji, Zahraa, et al. "Integrating segmentation with deep learning for
enhanced classification of epithelial and stromal tissues in H&E images."
Pattern Recognition Letters (2017).
• Bulten, Wouter, et al. "Automated segmentation of epithelial tissue in
prostatectomy slides using deep learning." Medical Imaging 2018: Digital
Pathology. Vol. 10581. International Society for Optics and Photonics,
2018.
• Chen, Hao, et al. "DCAN: Deep contour-aware networks for object
instance segmentation from histology images." Medical image analysis 36
(2017): 135-146.
• Cui, Yuxin, et al. "A Deep Learning Algorithm for One-step Contour
Aware Nuclei Segmentation of Histopathological Images." arXiv preprint
arXiv:1803.02786 (2018).
32. CONTD.,
• Dabass, Manju, Rekha Vig, and Sharda Vashisth. "Review of
Histopathological Image Segmentation via current Deep Learning
Approaches." 2018 4th International Conference on Computing
Communication and Automation (ICCCA). IEEE, 2018.
• Graham, Simon, and Nasir M. Rajpoot. "SAMS-NET: Stainaware multi-
scale network for instance-based nuclei segmentation in histology images."
Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International
Symposium on. IEEE, 2018.
• Janowczyk, Andrew, et al. "A resolution adaptive deep hierarchical
(RADHicaL) learning scheme applied to nuclear segmentation of digital
pathology images." Computer Methods in Biomechanics and Biomedical
Engineering: Imaging & Visualization 6.3 (2018): 270-276.
33. CONFERENCE
• Participated and presented a paper titled “A Survey of segmentation
Algorithm for segmenting Histopathology images” in the “International
Conference on Multidisciplinary research in global challenges and
perspectives of sustainable development”, held on 21st December 2019 at
St. jerome’s College, Nagercoil, Kanyakumari.
