Brain medical image diagnosis based on corners with importance-values

Abstract

Background: Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis.

Results: Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image.

Conclusions: In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

Keywords: Bipartite graph; Brain medical image diagnosis; Classification; Corner detection; Corner matching; Multilayer texture images.

Fig. 1

Workflow of the analysis. The sequence with the blue arrows depicts the training of the classifier with the corner response threshold θ in corner detection and with the K in the K-nearest neighbor model. The other sequence with the black arrows aims to test the trained classifier.

Fig. 2

Examples of “Normal” and “Abnormal” images. Brain MRI images in the first row belong to “Normal” category and that in the second row are “Abnormal” ones. Brain CT images in the third row are “Normal” and that in the fourth row are “Abnormal”

Fig. 3.

Example of normalizing a brain CT image a Original image I. b Image with the extracted intracranial portion. c Rotated image. d Image with its vertical external matrix. e Normalized grayscale image GI.

Fig. 4

Example of corner detection over a MTI. a GI. b MTI of the GI. c Detected Corners mapped to the GI.

Fig. 5

Corner matching example. a Corner sequence C b Corner sequence C' c Overlaying of C and C'. d Bipartite graph G based on C and C'. e Final matching result of the G

Fig. 6

Accuracy of the validation images using the proposed method. K is the parameter of the KNN model and θ is the corner response threshold for corner detection. a Dct. b Dmri.

Fig. 7

Accuracy of the validation images using the Harris-based method. K is the parameter of the KNN model and θ is the corner response threshold for corner detection. a Dct. b Dmri.