Diagnosis of Liver Tumor Using 3D Segmentation Method for Selective Internal Radiation Therapy

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1 Diagnosis of Liver Tumor Using 3D Segmentation Method for Selective Internal Radiation Therapy K. Geetha & S. Poonguzhali Department of Electronics and Communication Engineering, Campus of CEG Anna University, Chennai geethu_gct2006@yahoo.com, poongs@annauniv.ed Abstract This paper introduces 3-D liver segmentation method for selective internal radiation treatment as the treatment for liver tumors. In treatment of liver cancer, delivering maximum radiation dose to the tumor and minimum toxicity to the surrounding healthy tissue is of great difficult in clinical practice. This can be eliminated by 3-D segmentation method for accurate calculation of functional tumor volume and anatomical volume of the liver for determination of the tumor to normal liver ratio and consequently for the calculation of the dose to the patient. It uses region growing method with threshold algorithm. 3-D liver segmentation is vital in computer assisted surgery applications such as minimal invasive surgery, tumor resection, targeted drug delivery and donor liver transplantation. Keywords computed tomography, three dimensional, yttrium-90 I. INTRODUCTION Liver cancer (Hepatocellular carcinoma HCC) is the fourth most common cancer in the world. Every year there are 14,000 new patients affected by liver cancer. Liver cancer is malignant tumors that grow on the surface or inside the liver. It is caused by chronic hepatitis B or C infections, obesity, alcohol, diabetes and hemochromatosis. Various types of liver tumors are identified by abdomen CT scan. It can provide precise information about the size, shape and position of any type of tumors in the liver. CT scan is more preferred than MRI as it allows for evaluating extra-hepatic abdomen, comfortable, patient-friendly protocols, less cost and shorter examination time. To reduce the death rate due to liver cancer efficient treatment need to be provided to the patient. Current therapies include unresectable liver tumor, chemotherapy, transarterial chemoembolization (TACE), radiofrequency ablation (RFA) and Yttrium-90 radioembolization. To treat liver cancer, the best method used is selective internal radiation therapy (SIRT) with Yttrium-90 (Y- 90) microspheres is emerging as an effective liver-directed therapy. It is for the patients with unresectable cancers, especially metastasis to the liver. In SIRT, microspheres are loaded with radioactive substances and injected into the arteries that supply the tumor. The microspheres targets tumor with a high dose of radiation while sparing healthy liver tissue. It is outpatient procedure, less pain and low complication rate. The treatment involves the accurate calculation of functional tumor volumes and anatomical volumes of liver for the determination of the tumor to normal liver ratio and consequently for the calculation of the dose of Y-90 microspheres. The already existing methods, used morphological filters with region labeling algorithm resulted in 3% error rate in volume measurement and took more computational time [1].The segmentation based on shape constrained model resulted in not segmentation of larger lesions [2]. Although these techniques offers accurate results, the algorithms need to accommodate for data from different sources artifacts, varying protocols and presence of pathological structures such as tumors. From these studies major disadvantages are sensitivity to noise, improper segmentation and 3-D visualization are not taken into account. The motivation of this paper is to provide the means to perform the calculation of the volume to a high degree of accuracy with a much reduced computational time. The robust approach utilizes region growing based segmentation algorithm coupled with threshold algorithm specifically for CT datasets. II. PROCEDURE Liver segmentation based on CT image is a challenging task due to the presence of similar intensity objects in the abdomen with no clear delineation between these objects and the liver. A method for liver segmentation has been developed based on a 115

2 combination of region growing and threshold algorithm. Before proceeding to segmentation process, the image contrast is enhanced. The algorithm extract liver region and renders the segmented liver for 3-D viewing. The implementation strategy is shown in the Figure 1. with structuring element. The completely masked liver region is multiplied with the original input image and the liver region is obtained.the same process is repeated for all the slices of the dataset. 2) Threshold Algorithm: The threshold algorithm takes user defined mask as input. The intensity of the tumor region is determined. The region within the intensity range of the tumor is obtained by threshold algorithm. The morphological operation with structuring element is used to completely mask the tumor region. The tumor region is multiplied with the input slice image to get the tumor region. The same process is repeated for all the slices of the dataset. C. Area Calculation of Liver & Tumor: A. Image Pre-Processing Fig. 1 : General Algorithm Flow In the pre-processing step, the contrast of the image is improved for well differentiation of the liver from its surrounding soft tissues with the similar intensity. The contrast of the dicom images are enhanced in abdomen window using dicom image processing software. B. Design Structure of the Algorithm The algorithm is designed such that the user manually picks point of similar intensity in the scan for region growing segmentation. A rough outline of the liver in widely spaced slice is obtained from the CT image. From these initialization processes, the following automated steps are considered. 1) Region Growing Algorithm: Region growing is a procedure that group s pixels or sub regions into larger regions based on predefined criteria. Starting with a set of seed point, regions are grown from these by appending to each seed with neighboring pixels that have properties similar to the seed. The region is iteratively grown by comparing all unallocated neighboring pixels to the region. The difference between a pixel's intensity value and the region's mean is used as a measure of similarity. The smallest difference measured is allocated to the corresponding region. This process stops when the intensity difference between region mean and new pixel become larger than a certain threshold (T). Based on the threshold value, the mask of the liver region is obtained for the dataset. The liver region segmented by region growing algorithm is processed with morphological operations. In which the unmasked region is completely masked Area of liver and tumor is calculated in length units of centimeters (cm) by multiplying the total number of pixels in the region with the dimension of single pixel as shown in (1). Area of Liver/Tumor = A Total (1) where, A= Horizontal Resolution Vertical Resolution & Total = Total number of Pixels D. 3-D Rendering: The 3-D dataset are rendered using cost-effective software called 3D-Doctor. The software renders the segmented dataset in 3-D space and offers the possibility to view/edit/correct the rendered liver if necessary. The software also calculates the volume of the liver by determining the number of voxels that are masked as being within the liver region by the segmentation algorithm. The only input fed to the software is the segmented dataset and the original resolution of the CT dataset. The calculated volumes are in milliliters (ml). E. Dose Calculation of Yttrium-90: SIRT therapy is regarded as a regional treatment. SIR microspheres are loaded with the radionuclide yttrium-90.the dosage is calculated based on Body Surface Area (BSA) method. It is based on a whole liver approach and the patient specific dose (A yttrium-90 ) is given by (2). A yttrium-90 [Gbq] = (BSA-0.2) + V Tumor / V Total Liver (2) where, V Tumor = Volume of the total tumor mass in the liver V Total Liver = Volume of the total liver (inclusive tumor) BSA[m 2 ] = height[m] weight[kg]

3 III. RESULTS AND DISCUSSION To evaluate our proposed method, in this section, experimental segmentation of liver slices were carried out by using MATLAB 10. The first part of this section provides the segmentation results of the CT slices. The second part provides the area of liver and tumor. The third part provides the 3-D volume rendering with precise dose to be delivered. A. Segmentation: The image data has been acquired using CT system. The images are provided in dicom format with pixel matrix. For each scan, a stack of 68 slices covering the liver were acquired. The slice thickness is of mm. Figure 2 shows the segmented results obtained for particular dataset using region growing algorithm. 2(a) 2(c) 2(b) Fig. 2 : Segmented Liver Region. (a) Original Image, (b) Morphological Image, (c) Liver Region. Figure 3 displays the result of tumor segmented from the liver region using threshold algorithm. The tumor regions are accurately identified from the CT image. 3(a) 3(b) Fig. 3 : Segmented Tumor Region. (a) Threshold Image, (b) Tumor Region B. Area of Liver and Tumor: TABLE I AREA OF LIVER & TUMOR Slice Area of liver(cm 2 Area of ) No. tumor(cm 2 )

4 The area of the liver and tumor is calculated for 68 CT slice. Table I shows the area calculated for 22 slices in a dataset. In the table, the liver region starts shown from the slice number 1 and grows to its full length and finally it diminishes. This could be very well identified from the area of liver shown in the Table I. The tumor region is not shown in the beginning and end of the scanned slices. The area of the tumor varies from slice to slice based on the depth of spread of cancer cells. The horizontal and vertical resolution of the image is obtained from the detailed property window of the image. From the horizontal and vertical resolution, dimension of the single pixel can be determined. The number of pixels is found by using matlab function on binary image of the region. C. 3-D Volume Rendering: Results of the 3D rendering process obtained from the 3D doctor are shown in Figure 4.The rendering shown here has smooth surface modeling and volume rendering. The presence of tumor in liver is very well visible in surface model. The 3-D rendering is generated with translucent surfaces with varying opacities and colors. 4(a) 4(b) Fig. 4 : 3D model of Liver, (a) Surface Model, (b) Volume Rendering of liver Table II shows the volume of the liver and tumor obtained from 3D volume rendering. TABLE II VOLUME CALCULATED FOR LIVER & TUMOR Dataset No. Volume of liver(ml) Volume of tumor(ml) The obtained results are used to calculate the precise amount of yttrium-90 that is to be delivered through hepatic artery to kill the cancer cells. The radiation dose for the dataset is calculated by using the Equation (2). IV. CONCLUSION The accurate segmentation of liver and tumor from CT images were demonstrated in this study. The results of segmentation are verified by the expert. SIRT involves accurate volume calculation which is specific to each patient s anatomy to deliver absolute precise dose to the patient. From this work the radiation dose to be delivered by SIRT is GBq, while the other radiotherapy requires 80Gy. Experiment proves that the proposed method is effective to minimize the risk of damaging the healthy surrounding liver tissues. V. ACKNOWLEDGMENT The authors are grateful to Dr. Emmanvel, managing director of BHARAT SCANS for proving the CT liver images and the required clinical information. VI. REFERENCES [1] Lim S.J, Y.Y.Jeong, Y.S.Ho (2005) Automatic liver segmentation for volume measurement in CT images, IEEE Trans. In Image Processing [2] Krishnamurthy A, Nehrbass J, Chaves J. C, and Samsi S, (2007) Survey of parallel MATLAB techniques and applications to signal and image processing, in Proc. IEEE Int. Conf. Acoust., Speech Signal Process, Honolulu, Hawaii, U.S.A. [3] Lankton S and Tannenbaum A (2008) Localizing region-based active contours, IEEE Trans. Image Process., vol. 17, no. 11, pp [4] Rusk o L, Bekes G, Nemeth G, and Fidrich M (2007) Fully automatic liver segmentation for contrastenhanced CT images, in Proc. MICCAI Workshop on 3-D Segmentation. Clinic: A Grand Challenge, Brisbane, Australia. [5] Susomboon R, Raicu D. S, and Furst J (2007) A hybrid approach for liver segmentation, in Proc. MICCAI Workshop on 3-D Segmentation. Clinic: A Grand Challenge, Brisbane, Australia, 2007, pp [6] Massoptier L and Casciaro S (2008) A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans in IEEE EMBS Conference on Biomedical Engineering and sciences(iecbes 2008),Malaysia,pp , 2 nd December [7] Mohammed Goryawala,R.Guillen (2012) A 3-D Liver Segmentation Method with Parallel Computing 118

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