Nodule detection in lung using multi-threshold segmentation 
Sakshi Wasnik1, Pallavi Parlewar2, Prashant Nimbalkar3
1Sakshi Wasnik, Electronics Engineering Department, Shri. Ramdeobaba College of Engineering and Management, Nagpur, India
2Pallavi Parlewar, Electronics and Communication Engineering Department, Shri. Ramdeobaba College of Engineering and Management, Nagpur, India
3Dr. Prashant Nimbalkar, Radiologer, Precision Scan and Research Centre, Nagpur, India.
Manuscript received on 14 June 2019 | Revised Manuscript received on 20 July 2019 | Manuscript published on 30 July 2019 | PP: 271-276 | Volume-8 Issue-9, July 2019 | Retrieval Number: H7178068819/19©BEIESP | DOI: 10.35940/ijitee.H7178.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Presence of nodules in lung images can be an indication of multiple types of diseases such as tumor, cancer, etc. Detection of nodules for lung images is a ubiquitous task, which requires lot of computations for pre-processing, tissue detection, removal of non-nodule regions and finally nodule segmentation. In this paper we propose a multi-threshold descriptor based algorithm which applies multiple levels of thresholds to the image, in order to detect and remove all the non-nodule regions and finally uses KNN algorithm in order to classify the input image into benign or malignant. The training and testing sets are carefully selected in order to obtain optimal accuracy for the system. In this work, we obtain 82.65% accuracy, sensitivity and specificity is 85.71% and 80.35% respectively for classification of the input medical image.
Keywords: Classification, KNN algorithm, multi-threshold, nodule.
Scope of the Article: Classification