A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

Abstract

The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support. Graphical abstract.

Keywords: Common CT imaging signs of lung diseases (CISL); Lung CT image; Medical image retrieval; Multi-level; Similarity measure.

Fig. 1

An overview of the proposed multi-level similarity retrieval method

Fig. 2

Illustration of AE

Fig. 3

The instances of nine CISLs categories; the smaller rectangular boxes in lung CT images are magnified to show the details of the images

Fig. 4

The mini-batch mean-squared error with the different dimensions of attribute features in fivefold evaluation

Fig. 5

The ratios of intra-class distance to inter-class distance based on the different weights of the visual similarity (w_V) and attribute similarity (w_A) in the fivefold cross-validation experiments

Fig. 6

Average p@n values obtained by the similarity measure with the single level (M_V, M_A, and M_S), two levels (M_VA, M_VS, and M_AS), and our multiple levels (MLS)

Fig. 7

Average p@n values by our method MLS and the compared method FCSS [39]

Fig. 8

PR graph from our MLS and the compared method FCSS

Fig. 9

Retrieved top 10 similar images for given query image using our method MLS and the compared method FCSS, where the red boxes indicate irrelevant images and the others are relevant

Fig. 10

Retrieved and re-retrieved top 10 similar images for given query image, where the red dotted boxes are marked by user as the irrelevant images and the others are relevant

Fig. 11

Average p@n values from the MLS retrieval and re-MLS retrieval results