A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification

Year 2021, Volume: 5 Issue: 2, 281 - 291, 15.08.2021

Ali Çınar , Bengisu Topuz Semih Ergin

https://doi.org/10.35860/iarej.857579

Cited By: 2

Abstract

Suspicious regions in chest x-rays are detected automatically, and these regions are classified into three types, including “malignant nodule”, “benign nodule”, and “no nodule” in this study. Firstly, the areas except the lung tissues are removed in each chest x-ray using the thresholding method. Then, Poisson noise was removed from the images by applying the gradient filter. Ribs may overlap onto nodules. Since this circumstance will make the detection of a nodule difficult, it is necessary to distinguish and suppress the ribs. The location of the rib bones is determined by a template matching method, and then the corresponding bones are suppressed by applying the Gabor filter. After this stage, suspicious tissues in the chest x-rays are specified using the Chan-Vese active contour without edges. Then, some features are extracted from these suspicious regions. Six different features are extracted: Statistical, Histogram of Oriented Gradients (HOG)-based, Local Binary Pattern (LBP)-based, Geometrical, Gray Level Co-Occurrence Matrix (GLCM) Texture-based and Dense Scale Invariant Feature Transform (DSIFT)-based. Then, the classification stage is achieved using these features. The best classification result is obtained using statistical, LBP-based, and HOG-Based features. The classification results are evaluated with sensitivity, accuracy, and specificity analyses. K-Nearest Neighbour (KNN), Decision Tree (DT), Random Forest (RF), Logistic Linear Classifier (LLC), Support Vector Machines (SVM), Fisher’s Linear Discriminant Analysis (FLDA), and Naive Bayes (NB) methods are used for the classification purpose separately. The random forest classifier gives the best results with 57% sensitivity, 66% accuracy, 81% specificity values.

Keywords

Chest x-ray classification, Nodule classification, Nodule detection, Rib detection, Rib suppression, ROI detection

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