Abstract
The swift imaging speed, improved spatial resolution and lower cost compared to the magnetic resonance imaging is the reason behind the increased popularity of the computed axial tomography. In case of the esophageal tumor. The computed axial tomography utilizes the X-ray images to deliver the comprehensive images of the esophagus and the tissues surrounding it. It enables the detection of tumors or structural changes even at the developing stage and also gives the clear picture of the other distant organs like, lungs or liver affected by the cancer in the developed stage. One of the primary challenging chore in segmenting the esophageal tumor is because of the continuous variations in the position and the texture, intensity and as well as shape causing complexities in developing an standard procedure that could be applied universally. In this paper a semi-automated scheme is utilized in the segmentation of the esophageal tumors observed form the X-ray images of the computed axial tomography. An active- contour based semi-automated segmentation along with the procedures of level set is followed in the paper to segment the affected areas from the images of the esophagus acquired form the computed axial tomography. The strategy put forward segregates the work carried out into four major phases. The first phase extracts the images using the seed points. Second phase removes the unnecessary portions in the images, the threshold values are set in the third phase and the post processing is carried out in the fourth phase. This concept was evaluated on the real life data set of tumors collected from the nearby cancer treatment hospitals. The efficiency of the proposed strategy was compared with the previously existing state of art methods on the basis of the dice similarity, mean, medium and the maximal surface distance, and the Jaccard similarity. The concept put forth minimize the time utilization and also allows to have enhanced visualization of the tumors in the esophagus.
References
https://www.seattlecca.org/diseases/esophageal-cancer/esophageal-cancer-facts/esophagus
Gibson, Eli, Francesco Giganti, Yipeng Hu, Ester Bonmati, Steve Bandula, Kurinchi Gurusamy, Brian Davidson, Stephen P. Pereira, Matthew J. Clarkson, and Dean C. Barratt. "Automatic multi-organ segmentation on abdominal CT with dense v-networks." IEEE transactions on medical imaging 37, no. 8 (2018): 1822-1834.
Carvalho, L. E., Antonio Carlos Sobieranski, and Aldo von Wangenheim. "3D segmentation algorithms for computerized tomographic imaging: a systematic literature review." Journal of digital imaging 31, no. 6 (2018): 799-850.
Koresh, Mr H. James Deva. "Quantization with Perception for Performance Improvement in HEVC for HDR Content." Journal of Innovative Image Processing (JIIP) 2, no. 01 (2020).
Lenchik, Leon, Laura Heacock, Ashley A. Weaver, Robert D. Boutin, Tessa S. Cook, Jason Itri, Christopher G. Filippi et al. "Automated segmentation of tissues using CT and MRI: a systematic review." Academic radiology 26, no. 12 (2019): 1695-1706.
Tam, Clara. "Machine Learning towards General Medical Image Segmentation." (2020).
Horie, Miho. "Quantitative Imaging Using Computed Tomography for Evaluation of Chronic Lung Allograft Dysfunction." PhD diss., 2019.
Manoharan, Samuel. "A Smart Image Processing Algorithm for Text Recognition Information Extraction and Vocalization for the Visually Challenged." Journal of Innovative Image Processing (JIIP) 1, no. 01 (2019): 31-38.
Smith, Andrew. "Method for the detection and staging of liver fibrosis from image acquired data." U.S. Patent Application 16/196,158, filed March 21, 2019.
Islam, Abm Rezbaul, Ali Alammari, and Bill Buckles. "Skin detection in image and video founded in clustering and region growing." In Mobile Multimedia/Image Processing, Security, and Applications 2019, vol. 10993, p. 109930V. International Society for Optics and Photonics, 2019.
Bindhu, V. "Biomedical Image Analysis using Semantic Segmentation." Journal of Innovative Image Processing (JIIP) 1, no. 02 (2019): 91-101.
Anshad, PY Muhammed, S. S. Kumar, and Shajeem Shahudheen. "Segmentation of chondroblastoma from medical images using modified region growing algorithm." Cluster Computing 22, no. 6 (2019): 13437-13444.
Teng, Lin, Hang Li, Shoulin Yin, and Yang Sun. "Improved krill group-based region growing algorithm for image segmentation." International Journal of Image and Data Fusion 10, no. 4 (2019): 327-341.
Anter, Ahmed M., and Aboul Ella Hassenian. "CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm." Artificial intelligence in medicine 97 (2019): 105-117.
Song, Xiao, Gaoshan Deng, Yuanying Zhuang, and Nianyin Zeng. "An Improved Confidence Connected Liver Segmentation Method Based on Three Views of CT Images." IEEE Access 7 (2019): 58429-58434.
Sathesh, A. "Performance Analysis of Granular Computing Model in Soft Computing Paradigm For Monitoring Of Fetal Echocardiography." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 14-23.
Dias, Philipe Ambrozio, and Henry Medeiros. "Semantic segmentation refinement by Monte Carlo region growing of high confidence detections." In Asian Conference on Computer Vision, pp. 131-146. Springer, Cham, 2018.
Nagesh, A. Sri, Dr GPS Varma, and Dr A. Govardhan. "An improved iterative watershed and morphological transformation techniques for segmentation of microarray images." IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications” CASCT 2 (2010): 77-87.
Fourati, Wafa Abid, and Mohamed Salim Bouhlel. "Trabecular bone image segmentation using iterative watershed and multi resolution analysis." International Journal of Bio-Science and Bio-Technology 3, no. 2 (2011): 71-82.
Masoumi, Hassan, Alireza Behrad, Mohammad Ali Pourmina, and Alireza Roosta. "Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network." Biomedical signal processing and control 7, no. 5 (2012): 429-437.
Yazdanpanah, Azadeh, Ghassan Hamarneh, Benjamin R. Smith, and Marinko V. Sarunic. "Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach." IEEE transactions on medical imaging 30, no. 2 (2010): 484-496.
Karami, Ebrahim, Mohamed Shehata, Peter McGuire, and Andrew Smith. "A semi-automated technique for internal jugular vein segmentation in ultrasound images using active contours." In 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 184-187. IEEE, 2016.
