V01i031122

International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 1, Issue 3, November 2014

Segmentation and Interpretation of Solid Lesions in Ct Using Volume Estimation and Classification Janani.B1, Mr.B.S.Sathishkumar2 M.E student, Department of ECE, A.V.C.College of Engineering, Mannampandal, Tamilnadu1 Associate Professor, Department of ECE, A.V.C.College of Engineering, Mannampandal, Tamilnadu 2 Abstract: In medical application, the change of tumor’s size is an important issue for monitoring the cancer therapeutics.

Estimating the volume of a tumor need its description in 3-D which is called segmentation. This system used to find the tumor area on the captured liver image which has been collecting from the ct scan report. Here region-based segmentation technique is used to extract the images into homogeneous segments or regions to ease the analysis of image. Although, simply calculating the voxels within a segmentation result can show to ample changes in the volume, if the lesion has been segmented delicate differently by various segmentation technique or different scan. In this system gives a fast, generic algorithm for calculating the volume of tumor from ct scan image that regards partial volume correction at the edge of a segmentation result. It can be applied to homogeneous lesions and inhomogeneous lesions. In 3-D image, due to limited spatial resolution some voxels will loss at the border of the segmentation output, to overcome these problem pvc is used.

Finally these algorithms provide a more accurate estimation of tumor volume.

Keywords: CT, partial volume correction, segmentation, spatial subdivision, lesion. single intensity and background regions with different intensities. In this method, tumor size is being calculated by An oncology is a subordinate of medicine that deals the diameter of the lesion. Estimated the value only for with tumor. An oncology is related with therapy, diagnosis homogeneous lesions. There is no measurement about of any tumor and monitoring of tumor patients after accurate value of lesions volume. successful treatment. some of the technique are commonly used to diagnosis tumor such as X-RAY/MRI. In these B. Limitations systemic scan is used which takes several x-ray images a lot The main problem of threshold based approach is from different angle and to form 3-D.It provide more that they often lack the responsiveness and precision detailed view than others and also determine how much required for accurate classification. The problem gets rigid tumor affected nearby tissue. If the tumor size are shrink and in case of multi-modal histograms with no edged or wellalso no new arise, therapy is successful. A voxel couting defined boundaries. It is usually hard to define the practical defines the volume which is given by the number of all and numerical measures only on the basis of gray level voxels within the output of segmentation then multiplied by value. their volume.

II. PROPOSED SYSTEM A. Existing System In these proposed system, segmentation based In this existing system, volumetric CT scanning region approach and spatial subdivision which is used to using spiral or helical scanners has resulted in a revolution overcome the limitation of thresholding approach. From this for diagnostic imaging. In addition for future CT subdivision we extract all information that is necessary for applications, such as the detection of liver lesios have compensating the partial volume artifacts. substantially improved. In other words the isotropic 3-D voxel could not be analysed apart from some very specialized cases. Multi level thresholding through a numerical repeated algorithm.

The aspiration of thresholding is to selection those pixels from some image. Thus the objective of binarization is to mark pixels that belong to beginning regions with I.

INTRODUCTION

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 1, Issue 3, November 2014

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Finallly the result of the segmentation-based region approach is used to select the similarity pixels within the segmented image.

B. Spatial Subdivision Spatial subdivision which is the process of separating area into two or more uncoordinated subsets. A space is separated into several area and then the same space partitioning system is recursively given to each of the area thus created. Here quadtree decomposition divides a square image into four equal sized square blocks and then tests each block to see if it meets some criteria of congruity. If a block meets the rule it is not separated any further. If it does not meet the rule it is subdivided again into four blocks and the test criterion is applied to those blocks. This process is repeated iteratively until each block meet the rule.

Fig.1 Process Diagram of Volume Estimation

C. Partial Volume Correction

A. Region-Based Segmentation In the region approach, each pixel is assigned to a particular part. choose the seed points. If the approximate pixels of the initial seed points are severe the rule such as intensity, gray level. we use the rule of the same pixel value then check the adjacent pixels of the intial seed points. If their pixel values are uniform with seed points, they can be added to the seed points. It is stop until there is no change in two consecutive iterations. The basic idea of region splitting is to break the image into a set of disarrange regions, which are consistent within themselves.  Look at the area of interest and decide if all pixels contained in the region amuse some similarity constraint.  Initially take the image as a total to be the area of interest.  If true then the area of interest corresponds to an entire region in the image.  If false split the area of interest (usually into four equal subareas) and consider each of the subareas as the area of interest in turn.  .This process continues until no further splitting occurs.  If only a splitting is used then the final segmentation would probably contain many adjacent regions that have identical or similar properties. we need to merge these regions  If the adjacent regions satisfy the similarity properties, we will merge them.

It is mainly used to improve the accuracy and reproducibility of volumetric measurements for tumor in CT. Due to restricted spatial resolution, some voxels will loss at the edge of the segmentation result to overcome these problem partial volume correction is used. To calculate the part of each partial volume voxel that is part of the lesion.

For each voxel ,a volume is estimated by a linear combination of the allusion value of the connected with inner and outer tissue region of specific segment. For example, some cases with no difference between the lesion and its background the partial volume region has resemble values as the suitable tissue region. A small lesions, consider that the lesion made up of a single tissue region only. The average lesion value ‘l’ as reference value in the place of the inner tissue reference value

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III. EXPERIMENTAL RESULTS

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 1, Issue 3, November 2014

IV. CONCLUSION Estimating the volume of a tumor from CT image which is based on a region-based segmentation technique.PVC plays an important role for estimating a volume of lesion accurately and reproducibly. In these segmentation based partial volume correction which allows for improving the accuracy of volume estimation and the variation between the readers are reduced, and it give more reproducible volume estimation for liver metastases.pvc which is used for estimating the voxel at the edge of the segmentation result without missing a single voxel. To estimated the volume of tumor and also find whether the lesions are getting grow or shrink by using chemotheraphy in medical application.

V. FUTURE WORK To perform the features of tumor, which is an input of classifier by using neural network? According to rules, classifier which divides the features of tumor and it produces an output data. Finally an output data is compared with the collection of tumor dataset. With relevant information it is feasible to create such information comes in accessible during the time of surgical planning specially for sensitive areas as the Human Brain or the Spinal Cord.

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BIOGRAPHY

JANANI.B completed her B.E degree Instrumentation and Control Engineering from Anna University Chennai. Presently she doing M.E(Applied Electronics)in A.V.C.college of engineering. Her area of ACKNOWLEDGMENT interests in medical image processing, I extend my sincere thanks to Mr.B.S.Sathish process control and virtual instrumentation. kumar for their innovative ideas and suggestions throughout my project and the successful completion of project.

Mr.Sathish Kumar doing his Ph.D degree in Pondicherry University. He REFERENCES Completed his PG Degree in 2002 from [1]. Frank Heckel*, Hans Meine, Jan H. Moltz, Jan-Martin Kuhnigk, SASTRA University and UG degree in Johannes T. Heverhagen, Andreas Kießling, Boris Buerke, and 2001 from Bharadhidasan university. He Horst K. Hahn Segmentation-Based Partial Volume Correction is also the member in ISTE and BME.He forVolume Estimation of Solid Lesions in CT , IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 33, NO. 2, published a Paper in International and FEBRUARY 2014. national Journal and Conference.His area of interest in image processing and [2]. W. A. Weber, “Assessing tumor response to therapy,” J. Nucl.Med., vol. 50, no.Suppl 1, pp. 1S–10S, 2009. communication.

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