Cervical cancer histology image identification method based on texture and lesion area features

Figures & data

Figure 1. Overview of the whole image approach and calculation.

Figure 2. The CIN grading image samples. (a)Normal, (b) CINII, (c) CINIII, (d) Cervical Cancer.

Figure 3. (a) Original image, (b) Rotated original image, (c) Part of image from (b).

Figure 4. Euclidean distances transform. (a) The binary image I_c^B, (b) Distance transform image I_c^E.

Figure 5. The four types of points. (a) Regular point, (b) Branching point, (c) Boundary point, (d) Arc point.

Figure 6. Create medial axis. (a) The highest intensity pixel, (b) line transform based on step 2 and step 3.

Figure 7. Ten vertical image regions (L₁,L₂,…,L₁₀).

Figure 8. Contrast.

Figure 9. Correlation.

Figure 10. Entropy.

Figure 11. Homogeneity.

Figure 12. Angular Second Moment.

Figure 13. Marker-controlled watershed results with different thresholds. (a) T=15, (b) T=30, (c) T=45, (d) T=80.

Figure 14. The image segmentation. (a) original image, (b) image segmentation.

Figure 15. Morphological operation on the tissue. (a) Image with preliminary nuclei tissue, (b) Image with holes filling.

Figure 16. The result of image segmentation.

Figure 17. Three layers.

Figure 18. The area of top layer.

Figure 19. The area of middle layer.

Figure 20. The area of basal layer.

Table 1. The result of normal layers areas.

Vertical image	Basal area	Middle area	Top area	Vertical image	Basal area	Middle area	Top area
1	113	232	192	6	178	193	142
2	70	197	175	7	172	193	155
3	123	233	64	8	114	180	127
4	69	192	137	9	182	187	141
5	96	210	124	10	119	191	217

Table 2. The result of CIN lesion layers areas.

Vertical image	Basal area	Middle area	Top area	Vertical image	Basal area	Middle area	Top area
1	222	230	114	6	200	144	120
2	90	278	159	7	207	143	268
3	263	520	229	8	73	203	333
4	91	175	94	9	178	305	193
5	178	292	240	10	168	339	243

Table 3. The result of cancer layers areas.

Vertical image	Basal area	Middle area	Top area	Vertical image	Basal area	Middle area	Top area
1	235	230	164	6	775	144	474
2	150	278	686	7	659	143	646
3	249	520	450	8	618	203	517
4	413	175	649	9	814	305	1104
5	741	292	405	10	599	339	1138

Table 4. The recognition result of cervical cancer.

Feature	GLCM	Lesion Area	GLCM + Lesion Area	Current classification (%)
method	20/12	20/15	20/18	90%

Table 5. The result of different methods.

	Reference [4]		Reference [6]		The proposed method
Normal	20/18	90%	20/14	70%	20/18	90%
CIN lesion	20/12	60%	20/15	75%	20/14	70%
Cancer	20/16	80%	20/15	75%	20/18	90%

De S, Joe Stanley R, Lu C, et al. A fusion-based approach for uterine cervical cancer histology image classification. Comput Med Imaging Graph. 2013;37:475–487.

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