Modified Quality Threshold Clustering for Temporal Analysis and Classification of Lung Lesions

• Published in: IEEE transactions on image processing Vol. 28; no. 4; pp. 1813 - 1823
• Main Authors: Netto, Stelmo Magalhaes Barros, Bandeira Diniz, Joao Otavio, Silva, Aristofanes Correa, de Paiva, Anselmo Cardoso, Nunes, Rofolfo Acatauassu, Gattass, Marcelo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cancer; Classification; Clustering; Clustering algorithms; Density; Detection of changes in tissues; Diagnosis; Feature extraction; Lesions; Lung; lung lesion; medical image; Methodology; Nodules; Pulmonary lesions; quality threshold clustering; Statistical analysis; temporal analysis; Visualization; Volume measurement
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2018.2878954

Lung cancer is the type of cancer that most often kills after the initial diagnosis. To aid the specialist in its diagnosis, temporal evaluation is a potential tool for analyzing indeterminate lesions, which may be benign or malignant, during treatment. With this goal in mind, a methodology is herein proposed for the analysis, quantification, and visualization of changes in lung lesions. This methodology uses a modified version of the quality threshold clustering algorithm to associate each voxel of the lesion to a cluster, and changes in the lesion over time are defined in terms of voxel moves to another cluster. In addition, statistical features are extracted for classification of the lesion as benign or malignant. To develop the proposed methodology, two databases of pulmonary lesions were used, one for malignant lesions in treatment (public) and the other for indeterminate cases (private). We determined that the density change percentage varied from 6.22% to 36.93% of lesion volume in the public database of malignant lesions under treatment and from 19.98% to 38.81% in the private database of lung nodules. Additionally, other inter-cluster density change measures were obtained. These measures indicate the degree of change in the clusters and how each of them is abundant in relation to volume. From the statistical analysis of regions in which the density changes occurred, we were able to discriminate lung lesions with an accuracy of 98.41%, demonstrating that these changes can indicate the true nature of the lesion. In addition to visualizing the density changes occurring in lesions over time, we quantified these changes and analyzed the entire set through volumetry, which is the technique most commonly used to analyze changes in pulmonary lesions.