Final Project proposal DESIGN OF LUNG CANCER DETECTION SYSTEMS USING X-RAY IMAGE SEGMENTATION AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEMS (3)

5.    Research Metodology

5.1 Literature Review

       In lung cancer diagnosis, Imaging tests are performed to determine if a lung tumor is present.  Some imaging inspection can provide information that can help to determine whether a lung tumor is likely to be benign or malignant.  The final determination as to whether a tumor is cancerous can only be made by examining a tissue sample under a microscope.  Imaging tests are useful to look for enlargement of regional lymph nodes, which could indicate cancerous spread. A chest x-ray may also show enlarged lymph nodes, pneumonia, or blocked airways that are restrict air from reaching part of the lung. A lung tumor can be missed on chest x-ray if it is small or hidden behind a rib, collar bone, or the breastbone   

       Image segmentation can be applied in processing lung image from x-ray scan. Image segmentation divide images into its constituent regions. The level to which subdivision is carried depends on the problem being solved. Therefore, segmentation would stop when the object of interest in operations have been isolated. Some method for segmenting image are edge detection, line detecton using Hough transform, thresholding, region based segmentation, and watershed transformation.

To construct automatic detection whether lung cancer is present or not, ANFIS is used for Artificial Intelligence software. Adaptive-Network-based Fuzzy Inference System (ANFIS) is a Sugeno-like fuzzy system in a five-layered network structure. Back-propagation strategy is used to train the membership functions, while the last mean square algorithm determines the coefficients of the linear combinations in the consequent part of the model. Takagi and Sugeno type fuzzy if-then rules (TSK) are used in ANFIS model.

5.2 Data Collection

       In the proposed research, Scan result of lung image from X-Ray scan are collected as data input. Image processing stage is needed to convert image before it is used in ANFIS software for lung cancer prediction in imaging test phase.

5.3  Image Processing Phase

a.    Scanning

The purpose of scanning is to convert the original data to digital data. In the process of scanning, X-ray image of lung separated into left and right lung. This process is aimed to see the average detail of each side of the lung.

b.  Resizing

Scanned image of X-Ray lung cancer should be resized. Original image data will be resized in to 640 x 480 pixels. Objectives of image resizing is to reducing picture size and reducing time of processing.

c. Greyscaling

       Output from scanner can be loaded on software and detected as matrix. It will appear in the software matrix colour scale of x-rays. At this stage, grey scaling is needed to facilitate the computation on software by dividing RGB with 3.

e. Segmentation

The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.

f. Normalization

Normalization is the process of dividing all grey scaling value matrix with the largest value of the matrix to make all images input in the software has the equal size though brightness levels from different input, so that the mean results may apply to all image. All numbers are in matrix normalization ranged from 0 to 1

Figure 3 Image Processing Flowchart

5.4 ANFIS Design and Validation

The objective of ANFIS design is to obtain most suitable premise and consequent parameters applied in the software. Normalized image of lung used as input in the software. Image input is divided into approaches with 3 membership functions.. In order to make sure that the designed model is valid or not, data training process obtained to find the smallest error, then premise and consequent parameters can be determined in FIS editor

5.5 Software Design and Validation

After smallet error approached, the validated model parameter’s is implemented to design lung cancer detection software and validate it by  comparing doctor's diagnosis and the results of prediction software. Objective of the software validation is to measure the accuracy of the ANFIS software to predict lung cancer based on imaging test.

Figure 4 Flowchart of ANFIS

6.  Schedule of Research

       Timeline of Research is shown in Table 1 below

Table 1 Research Schedule

No	Activities	Month
		1	2	3	4
1	Literature Review
2	Data collection
3	Image processing software design
4	ANFIS software design
5	Training
6	Software validation
7	Data Analysis
8	Report Writing

7. References

1.        American Society of lung cancer. Lung Cancer Non-Small Cell Overview. American Cancer Society. 2011: 1.

2.        Dhillon D Paul, Snead David RJ. Advanced Diagnosis of Early Lung Cancer. 2007 : 57

3.        Floche. Background information Non-small Lung Cancer.[pdf]

(URL:http://www.roche.co.id/fmfiles/re7229001/Indonesian/media/background.library/oncology/lc/Lung.Cancer.Backgrounder.pdf accesed on July 30, 2011)

4.        Le Kim. Automated Detection of Early Lung Cancer and Tuberculosis Based on X Ray Image Analysis. International Conference on signal, speech, and Image Processing WSEAS. 2006. 110

5.        Al Daoud Essam. Cancer Diagnosis Using Modified Fuzzy Network. Universal Journal of Computer Science and Engineering Technology. 2010, 1(2) : 73

6.        Paryono Petrus. Citra Digital. [pdf]

(URL http://www2.ukdw.ac.id/kuliah/si/erickblog/...10E92/CitraDigital.pdf accessed on September 5, 2011)

7.        Feng Ding. Segmentation of Bone Structures in X-ray Images. School Computing.National University of Singapore. 2006 : 4

8.        Emy. Peningkatan Mutu Citra (Image Enhancement) pada Domain Spatial. 2007

9.        Kundra Haris, Verma Monika, Aashima. Filter for Removal of Impulse Noise by Using Fuzzy Logic. International Journal of Image Processing. 2005, 3(5) : 195-196

10.    Handburry Allan. A Short Introduction to Digital Image Processing. [html]

(URL http://cmm.ensmp.fr/~hanbury/intro_ip/ accessed on September 5, 2011)

11.    C Gonzales Rafaels, E Woods Richards, L Eddins Steven. Digital Image Processing using MATLAB.  Upper Sddle River. Pearson Prentice Hall

12.    Andhi Yudha M. Restorasi Citra Bintang Ganda Visual dengan Metode Blind Deconvolution Seddara. Institut Teknlogi Sepuluh Nopember : Engineering Physics Department,. 2010

13.    American Cancer Society. Lung Cancer. URL:http://cancer.org accesed on July 30, 2011

14.    American Society of Clinical Oncology. Guide to Lung Cancer. Alexandria. Conquer Cancer Foundation. 2011: 2.

15.    Anonymous. Kanker Paru Pedoman Diagnosis dan Penatalaksanaan di Indonesia. Perhimpunan Dokter Paru Indonesia. 2003

16.    Ayu Pradanawati Sylvia. Pengembangan Sistem Kecerdasan Buatan Berbasis Adaptive Neuro Fuzzy Inference System untuk Diagnosa Penyakit Kanker Paru. Institut Teknologi Sepuluh Nopember : Jurusan Teknik Fisika : 2010

17.    Reeve Dana. NCCN Guide Line for Patient. National Comperhensive Cancer Network. Fort Washington. 2010 : 9-11

18.    Kakar M, et all. Respiratory Motion Production by Using Adaptive Neuro Fuzzy Inference Systems (ANFIS). Institute of  Physics Publishing. 2005, 50 : 4722.

19.    Tahmasebi Pejman, Hezarkhani Ardeshir. Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation; CaseStudy, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran. Australian  Journal  of  Basic  and Applied  Sciences,  4(3): 2010 : 411

20.    Cruz Adriano. ANFIS : Adaptive Neuro Fuzzy Inference Systems. Mestrado NCE. 2006 : 6.