1. CERVICAL CANCER CLASSIFICATION
USING GABOR FILTERS
2011 First IEEE International Conference on Healthcare
Informatics, Imaging and Systems Biology
Advisor : Yin-Fu Huang
Student : Chen-Ju Lai

2. OUTLINE
 INTRODUCTION
 DATA COLLECTION
 METHODOLOGY AND RESULT
 CONCLUSION

3. INTRODUCTION
 Cervical cancer
 Biopsy test
 Cervical intraepithelial neoplasia (CIN)
 Input : histology images
Feature extraction : texture , using Gabor filter
Classification method : K-Means Clustering
Output : Normal/CIN1/CIN2/CIN3/Malignant
Pre-cancer

4. DATA COLLECTION
 Pathology anatomy laboratory of Saiful Anwar
hospital
 Biopsy images : resolution 4080 x 3072 pixels
(categorized by an expert pathologist)
 475 labelled images are used in this study
Normal CIN1 CIN2 CIN3 Malignant
60 70 50 50 245

5. DATA COLLECTION

6. CANCER
GRADING
METHODOLOGY
AND
RESULT

7. GABOR FILTER
Spatial domain
Gabor elementary function
2D Gaussian function
x'=x cos θ +y sin θ and y'=-x sin θ+y cos θ.
From (1) and (2), the Gabor elementary function
can be rewritten as
centre frequency
σx and σy are the spread of the Gaussian in x and y directions

8. GABOR FILTER
Frequency domain
 Assuming σx and σy are the same
u'=u cos θ +v sin θ and v'=u sin θ+v cos θ
v
(U,V) can decision (U0 ,θ)
V φ
U0
θ u
U

9. GABOR FILTER
 Sample
Original (a) f = 0.2,θ = 0 0 (b) f = 0.2,θ = 45 0
(c) f = 0.2,θ = 90 0 (d) f = 0.2,θ = 135 0

10. COMPARE TEMPLATE
 Compare each pixel with the templates.
 Supervised Training : generated templates
 24 distinctive Gabor filters are used to generate a
feature vector for each pixel and its neighbors.
background basal stroma normal abnormal
cells cells
500 pixels 500 pixels 500 pixels 500 pixels 500 pixels
average average average average average

11. SEGMENTED IMAGE & K-MEAN
CLUSTERING
 Segmentation
 After each pixel compare with the five feature vector
templates.
 blue : background , yellow : basal , white : stroma,
 green : normal cell , red : abnormal cell
 K-Means Clustering
 Based on the color.
 Quantify the normal nuclei and abnormal nuclei.

12. SEGMENTED IMAGE & K-MEAN
CLUSTERING

13. CALCULATE THE RATIO AND GRADING
 How to classify the image into categories ?
 Use the ratio of number of normal and abnormal cells.
Benign the number of abnormal cells < 5
CIN 1 ratio between abnormal and normal cells <
1/3
CIN 2 ratio between abnormal and normal cells
between 1/3 ~ 2/3
CIN 3 ratio between abnormal and normal cells
> 2/3 or full
Malignant ratio between abnormal and normal cells >
CIN 3

14. CALCULATE THE RATIO AND GRADING
 Table 1 shows the sample of the ratio between abnormal
and normal cell.

15. CALCULATE THE RATIO AND GRADING
 Table 2 shows the confusion matrix of the Gabor filter
hybrid with K-means clustering.
 The sensitivity of normal is 87%, CIN 1 is 86%, CIN 2 82
%,CIN 3 84% and malignant is 89%.
 The percentage of specificity of this system is 85%.
(52/60)=0.87
(60/70)=0.86
(41/50)=0.82
(42/50)=0.84
(219/245)=0.89

16. COMPARED WITH SERVAL METHOD
 Gray level Features , color K-mean and incremental
thresholding.

17. CONCLUSION
 A methodology of Gabor filter bank with hybrid K-
means clustering algorithm has been proposed.
 Designing Gabor filter bank with the optimum
selection parameters and different classification
method can improve performance using this
algorithm.