Chapter 4 Experiment and Discussion
4.1 The Results Using the Proposed Method to Ten Patients
4.1.2 The Results Using the Proposed Method in Choosing Six Body Parts
4.1.2 The Results Using the Proposed Method in Choosing Six
Body Parts.
In this section, we use the original CT to automatically get six body parts to be labeled with organ definition. These six labels are body of vertebra, spinal canal, right and left upper limb, left and right subscapularis muscle or head of rib. In this section, due to the enormous data image so we picked images from patient 1. The images chosen were the best case and the worst case to display and describe.
42
43
(a)
(b)
(b)
Figure 4.1 Best case of six body parts chosen (a) Original (b) Labeled image
In Figure 4.1, it is the most typical CT image that we want to process. The image of bones and organs are clearly visible. The results of this typical image would be fair.
Lastly through calculating our ideal coordinates, the differences were not bad. The
Euclidean distance was only 4.0181.
(a)
44
(b)
(b)
Figure 4.2 Worst case in choosing six body parts. (a)Original image (b)Image after labeling
As seen in Figure 4.2, the first is the worst case of six body parts chosen. This picture
clearly shows that the points chosen are invalid. This is not what we had expected. The Euclidean distance mean is 42.0539. The cause of this is simply that the body of vertebra was not taken clearly. It’s pixel’s gray level and the surrounding muscle and tissue’s gray level is about the same. As seen in Figure 4.2(a) where the red arrow head points.
4.1.3 The Results Using Proposed Method in Choosing Five
Parts.
Body
In this section, we will use the original CT to automatically five body parts. After that the definitions of human organs are labeled. The five labels are spinal canal, right and left upper limbs, right and left subscapularis muscle or head of rib. This can be seen in Figure 4.3.
(a)
45
(b)
Figure 4.3 Best case in choosing five body parts (a) Original image (b) Image after labeling
As seen in Figure 4.3 it shows the best case of choosing five body parts. The red arrow head in Figure 4.3(a) shows the body of vertebra. The body of vertebra in this image was not taken clearly. It will be eliminated by thresholding because it wasn’t taken clearly. This would not affect the result of choosing five body parts. Thus the five body parts chosen are much more accurate. This last image’s Euclidean distance mean is 3.4748.
46
47
(a)
(b)
Figure 4.4 Worst case in choosing five body parts (a) Original image (b) Image after labeling
As seen in Figure 4.4 it is the worst case in choosing five body parts. It is the opposite of the image above, where the body of vertebra is taken clearly. In Figure 4.4(b) the yellow arrow head shows that. The spinal canal’s original location is marked by the
48
red arrow head. Due to the K-means clustering was influenced by pixel around the body of vertebra. The chosen spinal canal will be deviated upwards. This would also deviate other chosen body parts. This image’s Euclidean distance mean is 37.6147.
4.2 Special Case: The Invasion of Metallic Items
According to the patient’s chest CT, during the experiments there will be numerous special images that would appear. These would influence the results of the experiments .Special situations are introduced below.
During experiments, some numbers were not acceptable, so images were closely monitored to see what the problem was. We discovered some CT images had human tissues and some non human tissues. These non human tissues were like metallic items or pipes that were inserted for medical use. This situation is seen in patient 8 and patient 9.
(a)
(b)
49
(c)
Figure 4.5 An unknown item invasion in Patient 8 (a) Original (b) Results from choosing 5 body part. (c) Results from choosing 6 body parts.
As seen in Figure 4.5(a) the red arrow head shows an invasion of an unknown item.
Due to unable to eliminate the unknown item, this influenced the results greatly.
Choosing five body part’s Euclidean distance mean were 45.0139 and the body parts where 6 were chosen had a Euclidean distance mean of 23.5105. There were some images that had unknown items invasion, had a mean over 100, this could influence greatly on the results.
50
Table 4.1 Obtaining five body parts, using pixel as an unit to calculate Euclidean
Patient1 3.4748 37.6147 13.1242 9.2494 Patient2 4.3778 40.4950 23.6917 13.4649 Patient3 2.7944 42.1344 26.2717 15.1543 Patient4 6.2081 45.7356 27.0521 16.5821 Patient5 3.3231 39.2526 15.7397 11.7670 Patient6 4.6041 47.3880 33.9643 12.2842 Patient7 6.3187 54.8082 40.6728 9.9252 Patient8 4.5551 125.0787 41.1615 38.9842 Patient9 22.7687 43.0277 33.7533 5.3916 Patient10 27.2089 45.3313 38.5092 3.5560
Male 4.5551 125.0787 35.6390 19.5601
Female 2.7944 42.1344 19.4075 13.4802
All patients
2.7944 125.0787 29.3781 19.12263
51
Table 4.2 Obtaining six body parts, using pixel as an unit to calculate Euclidean distance.
Patient1 4.0181 42.0539 21.9965 11.3501
Patient2 3.1319 12.2472 6.6272 2.4241
Patient3 3.7466 16.0136 7.7499 3.3473
Patient4 4.6648 21.8054 7.8267 2.9325
Patient5 4.5572 39.3277 15.4894 10.8374
Patient6 4.8097 14.6193 7.8810 2.6144
Patient7 3.0581 16.9723 9.9050 5.1280
Patient8 4.7910 119.4405 35.2707 39.4599 Patient9 3.9855 46.4590 13.9921 13.4462 Patient10 3.8667 10.9022 6.5793 1.5851
Male 3.0581 119.4405 13.6347 19.9956
Female 3.1319 42.0539 13.2783 10.3227
All patients
3.0581 119.4405 13.6354 17.1642
52
Table 4.3 Obtaining five body parts and eliminating the special case.
Male 4.6041 21.8054 35.6390 19.5601
Female 2.7944 42.1344 19.4075 13.4802
All patients
2.7944 42.1344 27.3544 15.2300
Table 4.4Obtaining six body parts and eliminating the special case.
Best
Male 3.0581 16.9723 8.0682 3.4776
Female 3.1319 42.0539 13.2783 10.3227
All patients
3.0581 42.0539 10.5582 7.9870
53
4.3 Conclusion
Figure 4.6 Patient 1 choosing six body part’s Euclidean distance histogram.
Figure 4.7 Patient 1 choosing five body part’s Euclidean distance histogram.
Lastly, we will use the image’s data to organize into tables as seen in Table 4.1 to Table 4.4. Table 4.1 represents the results of five body parts chosen and its average. Table 4.2 represents results of six body parts chosen and its average. Figure 4.6 and Figure 4.7 represent patient 1 choosing five and six body part’s Euclidean distance histogram.
54
55
Horizontal axis represents the number of images; the vertical axis represents the Euclidean distance. There are total 10 patient’s individual’s best and worst case and each individual’s average. The best and worst case is in each image’s five or six marks and the ideal five or six’s marks Euclidean distance’s mean and this mean is the biggest or the smallest. The data is also separate into female and males.
Data analysis shows that most chest CT images, six body parts are much accurate.
However, there are some patient’s images that some muscles or organs were not taken clearly. Most of these examples are body of vertebra was not taken clearly, this shows it would be best if five body parts are chosen. As seen in Figure 4.6 and 4.7, arrowheads represents 31 image, due to the body of vertebra was not taken clearly, so choosing five body parts is much more accurate than in choosing six.
Out of the 10 patients, Patient 8 and Patient 9 show outside invasions. After analyzing the results, outside invasions could probably is medical intravenous drip or medical instruments used in aiding the patient. This would affect the results. Eliminate patient 8 and nine to calculate the results as seen in Table 4.3 and Table 4.4. If eliminating patent 8 and 9 the average would much more acceptable. Thus, if five body parts are chosen, its Euclidean distance’s mean is 27.3544. If six body parts are chosen, its Euclidean distance’s mean is 10.5582. These results are much more acceptable.
It is important to note that the patient’s sex and our method also influence our data as seen in Table 4.3 and Table 4.4.
56
Chapter 5 Conclusion and Future Work
5.1 Conclusions
With unsteady improvement of technology, lifestyles of humans are getting more and more improvements. With industrialization shaping our society life, more and more diseases are becoming often visible. With improvements of food, the nutrients are lost during the improvements and the mass of pollution from industries, the chances of getting cancer is growing at a high rate. Although technology is improving, judgment in medical fields are still using X rays or CT to help doctor’s judgments.
In this paper, a simple method is introduced to automatically analysis chest CT and locating each organ to an improved atlas.
5.2 Future Works
The result accomplished in this thesis is only a preliminary of the image registration.
Much of work is needed to be done in the future. Some of the future works are:
(1) Further atlas
In this paper, due to the lack of ability and time, CT is only concentrated on six locations on the chest or organs. Using this method, more research can be conducted on more different areas.
(2) Choosing Chest photos and getting the automatic K value.
In this paper, it’s an automatically producing CT atlas, however, some parts is done
57
by human power. This is seen in choosing chest’s CT, using anatomy books as references to choose chest CT tools. Using K-means mathematically, automatically getting the K value [3] done with the photographs. These are not automatically done but done by humans.
(3) Three dimensionally images, Whole body atlas
In this thesis, the images we use are 2D. However, human organs are 3D. This would be lost as the images are only 2D. The properties will be lost during the transformation.
Wishing that juniors could transform the 2D into 3D [18, 21, 27, 29] for further research, and produce the whole body atlas. These atlases could be given to medical staffs for future judgments.
58
[12] 陳金山、徐淑媛編譯，L. H. Blackbourne, J. Antevil and C. Moore 著，2001，回 憶解剖學，195~199，台北:藝軒圖書出版社。
[13] 張豫雯，民 92，應用資料探勘挖掘潛在電子商務客戶群，大同大學資訊工程學 系碩士論文。
59
[20] A. T. Joseph, “Understanding the Standerdized Uptake Value, Its Methods, and Implications for Usage,” The Journal of Nuclear Medicine, Vol. 45, No. 9, pp.
1431-1434, 2004.
[21] G. Li, T. Liu, G. Young, L. Guo and S. T. C. Wong. “Deformation invariant attribute vector for 3D image registration: method and validation,” Biomedical Imaging:
Nano to Macro, 2006. in 3rd IEEE International Symposium, pp. 442-445, 2006.
[22] J. Y. Huang, P. F. Kao and Y. S. Chen, “A Set of Image Processing Algorithms for Computer-Aided Diagnosis in Nuclear Medicine Whole Body Bone Scan Image,”
IEEE Trans. on Nuclear Science, Vol. 54, No. 3, 2007.
[23] J. Serra, “Image Analysis and Mathematical Morphology,” Academic Press, London, 1982.
[24] M. Gudmundsson, E. A. E1-Kware and M. R. Kabuka, ＂Edge Detection in Medical Images Using a Genetic Algorithm Future,” IEEE Trans. on Medical Image, Vol.17, No.3, 1995.
60
[25] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.
[26] O. Demirkaya, “ Lesion Segmentation in Whole-Body Images of PET,” 2003 IEEE Nuclear Science Symposium Conference Record, Vol. 4, pp. 2873-2876, 2003.
[27] P. J. Besl and N. D. McKay, “A Method for Registration of 3-D Shapes,” IEEE Trans.
Patt. Anal. Machine Intell., Vol. 14, No. 2, pp. 239-256, 1992.
[28] S. Chen and R. M. Haralick, “Recursive Erosion, Dilation, Opening, and Closing Transforms,” IEEE Trans. Image Processing, Vol. 4, No. 3, 1995.
[29] S. Gefen, L. Bertrand, N. Kiryati, and J. Nissanov, “Localization of Sections Within the Brain Via 2D to 3D Image Registration,” Acoustics, Speech, and Signal Processing, 2005. Proceedings. IEEE International Conference, Vol. 2, pp.733-736, 2005.
[30] T. K. Yin and N. T. Chiu, “A computer-aided diagnosis for locating abnormalities in bone scintigraphy by a fuzzy system with a threestep inimization approach,”
IEEE Trans. Med. Imag. , vol. 23, pp. 639–654, 2004.
[31] R. C. Gonzalez and R. E. Woods, Digital Image Processing Second Edition. Prentice Hall, 2002.
[32] R. C. Gonzalez and R.E. Woods, Digital Image Processing Third Edition. Prentice Hall, 2008.
[33] R. M. Haralick, S. R. Stemberg, and X. Zhuang , “Image analysis using mathematical morphology,” IEEE Trans. Pattern Anal. Machine Intell. , Vol. 9, No.
4, pp. 532-550, 1987.