International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 9, No. 5, 8 Sep. 2019
Cover page and Table of Contents: PDF (size: 266KB)
Analysis of CT DICOM Image Segmentation for Abnormality Detection
Full Text (PDF, 266KB), PP.46-55
Views: 0 Downloads: 0
Author(s)
Rashmi Kulkarni
1,*
Bhavani K
1
Index Terms
Image processing, noise, filtration, image pre-processing, segmentation, nodule extraction.
Abstract
The cancer is a menacing disease. More care is required while diagnosing cancer disease. Mostly CT modality is used for Cancer therapy. Image processing techniques [1] can help doctors to diagnose easily and more accurately. Image pre-processing [2], segmentation methods [3] are used in extraction of cancerous nodules from CT images. Many researches have been done on segmentation of CT images with different algorithms, but they failed to reach 100% accuracy. This research work, proposes a model for analysis of CT image segmentation with filtered and without filtered images. And brings out the importance of pre-processing of CT images.
Cite This Paper
Rashmi Kulkarni, Bhavani K. "Analysis of CT DICOM Image Segmentation for Abnormality Detection", International Journal of Engineering and Manufacturing(IJEM), Vol.9, No.5, pp.46-55, 2019. DOI: 10.5815/ijem.2019.05.04
Reference
[1]Rafael C.Gonzalez, Richard E.Woods , “Digital Image processing”, 3rd edition ,Pearson publication,2013.
[2]Rashmi Kulkarni, Bhavani K, “A Survey on Noise Reduction and Segmentation Techniques in CT DICOM Images”, International Research Journal of Engineering and Technology (IRJET), Volume: 05 Issue: 05, May 2018.
[3]M.Jayanthi, “Comparative Study of Different Techniques Used for medical image Segmentation of Liver from Abdominal CT Scan”, IEEE WiSPNET conference,2016.
[4]Suren Makaju, P.W.C. Prasad, Abeer Alsadoon, A. K. Singh, A. Elchouemi, “Lung Cancer Detection using CT Scan Images”, 6th International Conference on Smart Computing and Communications, ICSCC 2017, 7-8 December 2017.
[5]Hasan Koyuncu, Rahime Ceylan, “A Hybrid Tool on Denoising and Enhancement of Abdominal CT Images before Organ & Tumour segmentation”, IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO),2017.
[6]Jiayong Yan, John Q. Fang, “Segmentation of Liver Metastasis on CT Images Using the Marker-controlled Watershed and Fuzzy Connectedness Algorithms”, IEEE 2016.
[7]Ashwani Kumar Yadav, Ratnadeep Roy, Rajkumar, Vaishali, Devendra Somwanshi, “Thresholding and Morphological Based Segmentation Techniques for Medical Images”, IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2016), December 23-25, 2016.
[8]M.Jayanthi, “Comparative Study of Different Techniques Used for medical image Segmentation of Liver from Abdominal CT Scan”, IEEE WiSPNET 2016.
[9]P.Arjun, M.K.Monisha, A.Mullai yarasi, G.Kavitha, “Analysis of the liver in CT images using an improved region growing technique”, 2015 International Conference on Industrial Instrumentation and Control (ICIC) Col/ege of Engineering Pune, India. May 28-30,2015.
[10]Ravindra Pal Singh, Manish Dixit, “Histogram Equalization: A Strong Technique for Image Enhancement”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.8, No.8 (2015), pp.345352,2015.
[11]Brij Bhan Singh, Shailendra Patel, “Efficient Medical Image Enhancement using CLAHE Enhancement and Wavelet Fusion”, International Journal of Computer Applications (0975 – 8887) Volume 167 – No.5, June 2017.
[12]Khobragade, S., Tiwari, A., Patil, C., Narke, V. , “Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network.” IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES),2016.
[13]Wuli Wang, Liming Duan, Yong Wang, “Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm”, Journal of Electrical and Computer Engineering Volume 2017, Article ID 1735176,2017.
[14]Smitha P, Shaji L, Dr. Mini M. G., “A Review of Medical Image Classification Techniques”, International Conference on VLSI, Communication & Instrumentation (ICVCI) , Proceedings published by International Journal of Computer Applications(IJCA),2011.
[15]Hetal J. Vala, Astha Baxi, “A Review on Otsu Image Segmentation Algorithm”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), ISSN: 2278 – 1323, Volume 2, Issue 2, February 2013.
[16]LIDC CT DICOM image dataset retrieved from : https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
[17] TCIA CT DICOM image dataset of Lung, Pancrease, Bladder, Stomach, Thyroid retrieved from: https://wiki.cancerimagingarchive.net/display/Public/Wiki
[18]Miah, M.B.A., & Yousuf, M.A. (2015) “Detection of lung cancer from CT image using image processing and neural network.” International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2015.