Multiple k-Nearest Neighbor Classifier and Its Application to Tissue Characterization of Coronary Plaque

Abstract

SUMMARY In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque.

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

“…The systematic search identified 1098 articles, with most records deemed outside the area of interest based on title and abstract review. Although several studies quoted large datasets, some were referencing total number of pixels [ 28 ], Region-Of-Interest (ROI) subsets extracted from small image numbers [ 29 ], or number of images extracted from a small number of patients [ 30 , 31 , 32 , 33 ]. To manage any potential bias and avoid inclusion of overfit or low variability datasets, articles where total unique patient numbers were less than or equal to 50 were also excluded.…”

“…Vascular pathology quantification was most often performed using CTA (27) and ultrasound (6) imaging, as well as several publications using other modalities (9). All but one IVUS study [ 34 ] was excluded due to insufficient patient numbers [ 28 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ], highlighting the need for more large-scale research in this modality. A few studies attempting vascular quantitation from Nuclear Magnetic Resonance (NMR) [ 42 ] and Nuclear Medicine (NM) [ 43 , 44 , 45 ] imaging were also included, as well as one publication using ICA [ 46 ].…”

“…As shown in Table 4 in contrast with CTA investigations, cardiac studies were comparatively few, with external ultrasound ill-suited to coronary vasculature investigation. Study sample sizes often included less than 50 patients and were too small for generalisation, particularly for highly invasive IVUS studies [ 28 , 36 , 39 , 40 ], although recently published works have attempted to address this [ 34 ].…”

Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules

“…Many have reported that mathematical models coupling with ultrasound characteristics could serve as a means to automatically discriminate among diseases 12 , 13 . Logistic regression (Logistics) 14 , support vector machine (SVM) 15 and artificial neural network (ANN) 16 , partial least squares discriminant analysis (PLS-DA) 17 , linear discriminant analysis (LDA) 18 , K-nearest neighbor (KNN) 19 , and random forest (RF) 20 are commonly used models for disease diagnosis. Logistics, PLS-DA and LDA belong to linear models, while ANN, KNN and RF belong to non-linear models 21 .…”