Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided
radiotherapy, non-invasive diagnosis, and treatment planning. Although numerous researches have been done in
developing various medical image fusion algorithms, the disadvantage of these approaches is that they lack universality in dealing with different kinds of medical images. To address this problem, we have proposed a novel method of medical image fusion using the spiking cortical model (SCM) for the first time. In the paper, the mathematical model of SCM is firstly described, and then image fusion algorithm with SCM is introduced in detail. To show that the SCM based fusion method can deal with multimodal medical images, we have used three pairs of medical images with different modalities in the simulation experiments and made comparisons among the proposed method and the state-of-art fusion methods such as Laplacian pyramid, Contrast pyramid, Morphological pyramid and Ratio pyramid. The performance of various methods is investigated using such image assessment metrics as Mutual Information (MI), the edge preservation values (QAB/F), the Local Structural Similarity (LSSIM) and the Universal Image Quality Index (UIQI). The experimental results show that our proposed method outperforms other methods in both visual effect and objective evaluation. It demonstrates that the SCM based method is a highly effective method for multi-modal medical image fusion due to its versatility and stability.
Proc. SPIE. 8669, Medical Imaging 2013: Image Processing
KEYWORDS: Ischemic stroke, Ultrasonography, Databases, Image segmentation, Image processing, Arteries, Feature extraction, Image classification, 3D image processing, Simulation of CCA and DLA aggregates
Carotid atherosclerosis is the major cause of ischemic stroke, a leading cause of mortality and disability. Many research
studies have been carried out on how to quantitatively evaluate local arterial effects of potential carotid disease
treatments. In this paper, the atorvastatin effect evaluation on atherosclerosis plaques are classified based on various
shape and texture features extracted from ultrasound images. First, images of atherosclerotic lesions were extracted
manually from ultrasound images by an expert physician. After analysis, 26 shape and 85 texture characteristics, and
vessel wall volume (VWV) percent of change, were extracted and calculated from atherosclerotic lesions. Among these,
to make the method convenient and exact enough, effective features and VWV percent of change, were selected for drug
treatment effect evaluation by physician. Finally, a support vector machine (SVM) classifier was utilized to classify
atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a
database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group)
for evaluation. The classification results showed overall accuracy 91.67%, sensitivity 95.56%, specificity 86.16%;
positive predictive value 90.72%, negative predictive value 93.20%, Matthew’s correlation coefficient 82.81%, Youden’s
index 81.72%. And the receiver operating characteristic (ROC) curve in our test also performed well. The experimental
results also demonstrate that classification using the combined features has higher accuracy than that only using
shape/texture feature or VWV percent of change. The proposed method can be used for the statins effect evaluation,
especially when patients are treated with drugs, and further be developed as a beneficial tool for facilitating a physician’s
diagnosis of the atherosclerosis.
Wavelet parameters (e.g., wavelet type, level of decomposition) affect the performance of the wavelet denoising algorithm in hyperspectral applications. Current studies select the best wavelet parameters for a single spectral curve by comparing similarity criteria such as spectral angle (SA). However, the method to find the best parameters for a spectral library that contains multiple spectra has not been studied. In this paper, a criterion named normalized spectral angle (NSA) is proposed. By comparing NSA, the best combination of parameters for a spectral library can be selected. Moreover, a fast algorithm based on threshold constraint and machine learning is developed to reduce the time of a full search. After several iterations of learning, the combination of parameters that constantly surpasses a threshold is selected. The experiments proved that by using the NSA criterion, the SA values decreased significantly, and the fast algorithm could save 80% time consumption, while the denoising performance was not obviously impaired.