Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Medical Image Fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

A Computed Tomography (CT) scan is the most used technique for distinguishing harmful lung cancer nodules. A Computer Aided Diagnosis (CAD) framework for the recognition of lung nodules in thoracic CT pictures is implemented. A lung nodule which can be either benign or malignant can be easily classified as the better support for treatment. The proposed work is based on using Wavelet feature descriptor and combined with an Artificial Neural network for classification. The computed statistical attributes such as Autocorrelation, Entropy, Contrast, and Energy are obtained after applying wavelet transform and used as input parameters for neural network Classifier. The NN Classifier is designed by considering training functions (Traingd, Traingda, Traingdm, and Traingdx) using feed forward neural network and feed forward back propagation network. The feed forward back propagation neural network gives better classification results than feed forward. The proposed classifier produced Accuracy of 92.6%, specificity of 100% and sensitivity of 91.2% and a mean square error of 0.978.

Early Detection of Lung Cancer Using Wavelet Feature Descriptor and Feed Forward Back Propagation Neural Networks Classifier

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

/pdf/automated-system-for-lung-nodules-classification-based-on-31epx296ff.pdf

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

Lung cancer is the major cause of death among patients with cancer throughout the world. The main symptom that indicate the lung cancer is the presence of lung nodules. This work proposes a methodology to classify lung nodule and non-nodule using texture features. The state-of-art of the presented work are the adaption of the Artificial Crawlers and Rose Diagram techniques for representing patterns over 3D images. Several information are extracted based on the texture behavior of these methods, allowing the correct classification of lung nodules candidates using Support Vector. Objective: This work proposes a methodology to classify lung nodule candidates and non-nodule candidates based on computed tomography (CT) images. Methodology: The Lung Image Database Consortium (LIDC-IDRI) image database is employed for our tests. Three techniques are employed to extract texture measurements. The first technique is artificial crawlers (ACs), an artificial life algorithm. The second technique uses the rose diagram (RD) to extract directional measurements. The third technique is a hybrid model that combines texture measurements from artificial crawlers and the rose diagram. The support vector machine (SVM) classifier with a radial basis kernel is employed. Results: In the testing stage, we used 833 scans from the LIDC-IDRI database. For the application of the methodology, we decided to divide the whole database into two groups, training and testing. We used partitions of training and testing of 20/80%, 40/60%, 60/40% and 80/20%. The division was repeated 5 times at random. We reached a mean accuracy (mACC) of 94.30%, a mean sensitivity (mSEN) of 91.86%, a mean specificity (mSPC) of 94.78%, a coefficient of accuracy variance (CAv) of 1.61% and a mean area under the receiver operating characteristic (mROC) curves of 0.922. Conclusion: Lung cancer has the highest mortality rate and one of the smallest survival rates after diagnosis. An early diagnosis increases the survival chance of patients. The proposed methodology is a useful tool for specialists in the detection of nodules. We believe we contribute for the expert system field because 1) the adaption of the Artificial Crawlers and Rose Diagram methods as 3D texture descriptors is innovative and contains great potential; 2) we adapted and developed measurements from the 3D texture descriptors; and 3) the simplicity and discriminative power of the methodology can be extended to applications based on images with other contexts.

Lung nodule classification using artificial crawlers, directional texture and support vector machine

In order to classify lung nodules, an approach combining rule-based and SVM is proposed in the paper. Firstly, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; the approach combining rule-based and SVM has higher omission than SVM, but lower misclassification. The causes of nodules omission and misclassification are summarized and the solution is discussed in the paper at last.

Lung nodule classification combining rule-based and SVM

Multi-modal medical image fusion has important value in medical diagnosis. In this paper, the multi-resolution analysis of biorthogonal wavelet transform is introduced for CT and PET image fusion, then a new fusion algorithm with the combination of local standard deviation and energy as texture measurement is presented. Experiments show that both anatomical and metabolism information can be obtained effectively, and both the edge and texture features can be reserved successfully.

Multi-modal Medical Image Fusion Based On Wavelet Transform And Texture Measure

Poor illumination, less background contrast and blurring effects makes the medical, satellite and camera images difficult to visualize. Image fusion plays the vital role to enhance image quality by resolving the above issues and reducing the image quantity. The combination of spatial and spectral technique Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) decrease processing time and reduce number of dimensions but down sampling causes lack of shift invariance that results in poor quality final fused image. At first this work uses combined median and average filter that eliminates noise in the image which is caused by illumination, camera circuitry and sensor at preprocessing stage. Then, hybrid Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) technique is implemented to increase output image accuracy by eliminating down sampling and is not influenced by artifacts and blurring effects. Further, it can overcome the trade-off of Heisenberg’s uncertainty principle by improving accuracy in both domains, time (spatial) as well as frequency (spectral). The proposed combined median and average filter with hybrid SWT-PCA algorithm measures quality parameters, such as peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC) and improved results depict the superiority of the algorithm than existing techniques.

/pdf/multi-focus-image-fusion-using-combined-median-and-average-3he2hu2mb8.pdf

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

Image enhancement is a challenging task in image analysis particularly, it is more challenging in performing image fusion. Image fusion is the process of combining multiple images to produce quality output without any variation in contrast, blurring, and noise. Many image fusion algorithms have been implemented, but their final fused images suffer from variations in background contrast, uneven illumination, blurring, and the presence of noise. To overcome the aforementioned issues, this paper proposed a new image fusion method, which improves image contrast and also gives appropriate details of the image. Our method is based on a set of conventional techniques such as amalgamated histogram equalization and fast gray-scale grouping to handle the problems mentioned, and we improve overall fusion strategies by proposing a novel principal component analysis technique to convert RGB types images to high gray-scale contrast image as the final output image. We have carried out many experiments on different common databases used by various researchers. Our proposed method gives good subjective and objective performances compared to other statuses. Our proposed method can be used in different monitoring applications.

/pdf/an-enhanced-image-fusion-algorithm-by-combined-histogram-2ad3gxq6rj.pdf

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

When 3D conformal radiotherapy treatment planning (3D CRTP) is employed for tumor treatment, the relative position between the tumor and its adjacent tissues could be obtained easily through analysing the medical data sets which are fused the information of functional images and anatomical images. In order to implement effectively and efficiently the fusion of mass multimodal medical images data, a parallel multimodal medical image fusion method based on wavelet transform with fusion rule of combining the local standard deviation and energy is proposed. Experiments demonstrate the good performance of the proposed method.

Tian Lianfang

Papers

Lung nodule classification combining rule-based and SVM

Multi-modal Medical Image Fusion Based On Wavelet Transform And Texture Measure

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Parallel Multimodal Medical Image Fusion in 3D Conformal Radiotherapy Treatment Planning