An Efficient Liver Lesion Border Extraction for Liver Cancer Analysis Using SVM Approach

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International Journal of Electrical, Electronics and Computer Systems (IJEECS)

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ISSN (Online): 2347-2820, Volume -4, Issue-8, 2016 54

An Efficient Liver Lesion Border Extraction for Liver Cancer Analysis Using SVM Approach

1Pruthvi P R, ²Vartika Sharma, ³Syed Thouheed Ahmed

1,2Dept of CSE, GSSS IETW, Mysore

3ThinkSoft Research and Information Technologies, Bengaluru

Abstract: liver cancer is most dangerous cancer currently nominated under the research from world health organization. In this paper a machine learning approach for SVM classification based liver lessens detection technique is proposed. The system is tested and validated on pixel based clinically and AIMS certified datasets, the proposed system is fit for research and thus improves the ratio of detection. The system GUI based application design is discussed in the conclusion.

I. INTRODUCTION

The Liver is considered to the biggest human organ. As it is a large organ, its complexities of diagnosing any lump or lesions formation are not easier. Hence this domain has a wide range of opening for research and development. Many latest techniques have been proposed based on many Para-critical features of liver which includes size, dimensions and geometry in general. In recent era of advanced medical surgeries, it‟s important for a surgical planning system to detect the lumps and lesions. Detection of these lesions is achieved through various image segmentation techniques and algorithms. Performing this task of image segmentation shall be time consuming if it is done manually, hence we discuss about semi or full automation of the surgical planning system known as machine learning.

Machine learning is an approach of combing complex image processing algorithms which is intended to perform a complex operation in a dynamic machine state input. In order to mask images of liver under segmentation is considered to be a difficult task as the edges of liver are smoother and bulged with low contrast. Fetching an abnormal image of liver is made still more complicated as the pixel intensities of overall liver area remains same due to its neighboring organ overlapping in case of abnormalities. Hence fetching such image for processing shall lead

II. LITERATURE SURVEY

Accurate and realistic liver image segmentation is difficult and complex in computation for automatic surgical planning system with various causes discussed in session I (Introduction). Based on this we have studies various liver image segmentation as discussed below [2].

1. Based on Neural Network- In this approach, the proposed techniques used to calculate the region of liver and its surrounding organs based on intensity values and later this approach was enhanced by introducing textural detection and future these images we sampled with clustering for ROI classification and extraction of abnormalities in liver.

2. Support vector machine based technique- through this approach; liver images can be extracted with maximum accuracy in segmentation. This technique adds value to textural detection with wavelets for enhancement with respect to liver morphological characteristics. These textural descriptors are used to support vector machine for liver are classification and ignoring surrounding organs using pixel ranging technique [4]

3. Clustering Techniques- With this approach, liver images can be extracted and preprocessed which includes operations like removal of surrounding organ overlapping, fat layer removal and edge extraction of the liver overlapped within another organ. In this approach, removal of fat cells and mass is comparatively easier as it is globalized and extracted and masked based on lower intensity range values. Image classification is second step in this approach, which concludes images abnormality using patterns of clusters.

Hence we can draw a summarized conclusion on literature of various segmentation techniques as, manual liver segmentation is possible and remains safer in expertise consultation but it consumes more time for break down. Various image processing techniques improves the speed and performance of segregating liver images and they lesions. Machine learning is said to be one of the most important mile stone in automation of surgical planning system. Though segmentation with these approaches is faster but overall accuracy remains still a challenge.

III. SYSTEM METHODOLOGY

The proposed system includes the concept of SVM based pattern extraction of liver lessens and the discussed architectural diagram is shown in Fig 1.

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The proposed system data is acquired from a CT sources and thus the clinical data collected for research is authentic and medically fit for processing under technological research. On future processing the system realign the configuration and quality check for the input data samples. Under the process of preprocessing the regularized concept of refining and filtering is performed. This improves the processing range and thus improves the overall system processing quality.

Fig 1: Proposed System Block Diagram

Under filtering, the concept of filtering and image quality is suppressed and unsegment data is retrieved out under border extraction the input samples is fetched. The system is appended with SVM based classification for pattern recognition. The liver lessen is extracted from the sample, on successful detection of liver lesion the lump is extracted.

IV. IMPLEMENTATION

The system is simulated MATLAB 2012R and GUIDE for GUI creation, the system is well maintained with system proof of concept. The resultant system is shown in Fig 2.

Fig 2: Implementation of the Proposed System The proposed system simulation is shown here, it consist

of image collection and image splitting into two halfs under image segmentation approach. The overall input samples are analyzed with histogram of the two equal half images and then the difference is analyzed under

„haar‟ approach for threshold value analysis. The overall system now analyzes the area of image concentration for liver lesion detection. The system is re-constructed and simulated under SVM approach and final lessen / liver lump is extracted.

Algorithm: Liver Cancer Detection

1. Input Data set collection (I)

2. Preprocessing and Edge Detection (I)

3. Image Splitting and Histogram analysis (IR, IL) 4. Image merging and Collaboration for Processes matching

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If (FindHist(IR)>FindHist(IL)) Consider (IR)

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Else Consider (IL)

5. Load balancing and pattern initialization L=im2bw(im,.65);

6. Border value striming and Pattern extraction 7. Liver cancer lessen extraction

V. CONCLUSION

The output shown in fig 2 consists of a series of steps as discussed in system architecture. Each image represents a processing step for liver lesion extraction. The images acquired and processed are efficient and has gained a higher efficiency compared to the previous algorithms.

REFERENCE

[1] Mortele, K. J., Cantisani, V., Troisi, R., Preoperative Liver Donor Evaluation: Imaging and Pitfalls, Liver Transplantation, Vol 9, No9, Suppl 1,Sept, 2003: pp S6-S14.

[2] Ritupunia,. Review on Machine learning techniques on automatic live segmentation, Vol 3,issue 4, April 2013 pp. 666-670 IJARC&S [3] Bogetti, J. D., et al., Accuracy and utility of 3-

Dimensional computed tomography in evaluating

donors for adult living related liver transplants, Liver Transplantation, Vol. 7, Bo. 8, August, 2001, pp. 687-692..

[4] Cristianini, N., Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel- based Learning Methods, Cambridge University Press, ISBN 0521780195, 2000.

[5] Liu, J., Hu, Q., Chen, Z., and Heng, P., Adaptive Liver Segmentation from Multi-slice CT Scans, 7th Asian-PacificConference on Medical and Biological Engineering(APCMBE)2008, 22–25 April 2008, Beijing, China, pp. 381-384, 2008.

[6] D. Mahmoud-Ghoneim, M. K. Alkaabi, J. D. de Certaines and F. M Goettsche, “The impact of image dynamic range on texture classification of brain white matter,” BMC Med Imaging, vol.8(18), Dec, 2008.

[7] M. Mharib, A. R. Ramli, S. Mashohor, R. B.

Mahmood, “Survey on Liver CT image Segmentation Methods” in ArtifIntell Rev 37: pp.

83-95 Springer 2012.

[8] H. Masoumi, A. Behrad, M. Ali Pourmina, A.

Roosta, “Automatic Liver Segmentation in MR images using an iterative watershed algorithm and artificial neural network”, in Biomedical signal processing and control, pp. 429-437 Elsevier 2012.

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