Chih-Cheng Hung

Label fusion method based on sparse patch representation for the brain MRI image segmentation

The multi-Atlas patch-based label fusion method (MAS-PBM) has emerged as a promising technique for the magnetic resonance imaging (MRI) image segmentation. The state-of-the-art MAS-PBM approach measures the patch similarity between the target image and each atlas image using the features extracted from images intensity only. It is well known that each atlas consists of both MRI image and labelled image (which is also called the map). In other words, the map information is not used in calculating the similarity in the existing MAS-PBM. To improve the segmentation result, the authors propose an enhanced MAS-PBM in which the maps will be used for similarity measure. The first component of the proposed method is that an initial segmentation result (i.e. an appropriate map for the target) is obtained by using either the non-local-patch-based label fusion method (NPBM) or the sparse patch-based label fusion method (SPBM) based on the grey scales of patches. Then, the SPBM is applied again to obtain the finer segmentation based on the labels of patches. The authors called these two versions of the proposed fusion method as MAS-PBM-NPBM and MAS-PBM-SPBM. Experimental results show that more accurate segmentation results are achieved compared with those of the majority voting, NPBM, SPBM, STEPS and the hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition.

Myocardial motion estimation of tagged cardiac magnetic resonance images using tag motion constraints and multi-level b-splines interpolation.

Myocardial motion estimation of tagged cardiac magnetic resonance (TCMR) images is of great significance in clinical diagnosis and the treatment of heart disease. Currently, the harmonic phase analysis method (HARP) and the local sine-wave modeling method (SinMod) have been proven as two state-of-the-art motion estimation methods for TCMR images, since they can directly obtain the inter-frame motion displacement vector field (MDVF) with high accuracy and fast speed. By comparison, SinMod has better performance over HARP in terms of displacement detection, noise and artifacts reduction. However, the SinMod method has some drawbacks: 1) it is unable to estimate local displacements larger than half of the tag spacing; 2) it has observable errors in tracking of tag motion; and 3) the estimated MDVF usually has large local errors. To overcome these problems, we present a novel motion estimation method in this study. The proposed method tracks the motion of tags and then estimates the dense MDVF by using the interpolation. In this new method, a parameter estimation procedure for global motion is applied to match tag intersections between different frames, ensuring specific kinds of large displacements being correctly estimated. In addition, a strategy of tag motion constraints is applied to eliminate most of errors produced by inter-frame tracking of tags and the multi-level b-splines approximation algorithm is utilized, so as to enhance the local continuity and accuracy of the final MDVF. In the estimation of the motion displacement, our proposed method can obtain a more accurate MDVF compared with the SinMod method and our method can overcome the drawbacks of the SinMod method. However, the motion estimation accuracy of our method depends on the accuracy of tag lines detection and our method has a higher time complexity.

A Novel Endoscope System for Position Detection and Depth Estimation of the Ureter

Iatrogenic injury of ureter occurs occasionally in the clinical laparoscopic surgery. The ureter injury may cause the serious complications and kidney damage. To avoid such an injury, it is necessary to detect the ureter position in real-time. Currently, the endoscope cannot perform this type of function in detecting the ureter position in real-time. In order to have the real-time display of ureter position during the surgical operation, we propose a novel endoscope system which consists of a modified endoscope light and a new lumiontron tube with the LED light. The endoscope light is modified to detect the position of ureter by using our proposed dim target detection algorithm (DTDA). To make this new system functioning, two algorithmic approaches are proposed for the display of ureter position. The horizontal position of ureter is detected by the center line extraction method and the depth of ureter is estimated by the depth estimation method. Experimental results demonstrate that the proposed endoscope system can extract the position and depth information of ureter and exhibit superior performance in terms of accuracy and stabilization.

Reflection invariant local binary patterns for image texture classification

In this study we modified the local binary pattern operator (LBP) to obtain the robust invariant texture patterns for image texture classification. The modified method will be able to calculate patterns which are invariant for translation, scaling, rotation and reflection. Therefore, the modified LBP is called R-LBP. Although many variation of LBP have been proposed, most of them cannot detect and recognize the patterns of reflection. Both clockwise and counter clockwise coding is used in the proposed R-LBP operator in order to derive a minimum code to representing the pattern. Experimental results show that the proposed method is effective in determining the invariant patterns for image texture classification.

A target-oriented segmentation method for specific tissues in MRI images of the brain

The multi-atlas based segmentation method can achieve the accurate segmentation of specific tissues of the human brain in the magnetic resonance imaging (MRI). The correct image registration and fusion scheme used in this method have an impact on the accuracy of segmentation. Similar to any traditional rigid registration method, we use the same method in our proposed target-oriented registration for the coarse registration between the target image and atlas image. However, to improve the registration accuracy in the area to be segmented, we propose a target-oriented image registration method for the refinement. We employ the distribution probability of the tissue (to be segmented) in the sparse patch-based label fusion process. Our aim is to determine if the proposed registration method can contribute the segmentation accuracy and which label fusion method is a good fit with this target-oriented registration. To evaluate the efficiency of our proposed method, we compare the performance of the majority voting method (MV), the nonlocal patch-based method (Nonlocal-PBM) and the sparse patch-based method (Sparse-PBM). Experimental results show that more accurate segmentation results can be obtained with the proposed registration method in this study. This result can provide more accurate clinical diagnosis information.

An improved label fusion approach with sparse patch-based representation for MRI brain image segmentation

The multi‐atlas patch‐based label fusion (LF) method mainly focuses on the measurement of the patch similarity which is the comparison between the atlas patch and the target patch. To enhance the LF performance, the distribution probability about the target can be used during the LF process. Hence, we consider two LF schemes: in the first scheme, we keep the results of the interpolation so that we can obtain the labels of the atlas with discrete values (between 0 and 1) instead of binary values in the label propagation. In doing so, each atlas can be treated as a probability atlas. Second, we introduce the distribution probability of the tissue (to be segmented) in the sparse patch‐based LF process. Based on the probability of the tissue and sparse patch‐based representation, we propose three different LF methods which are called LF‐Method‐1, LF‐Method‐2, and LF‐Method‐3. In addition, an automated estimation method about the distribution probability of the tissue is also proposed. To evaluate the accuracy of our proposed LF methods, the methods were compared with those of the nonlocal patch‐based LF method (Nonlocal‐PBM), the sparse patch‐based LF method (Sparse‐PBM), majority voting method, similarity and truth estimation for propagated segmentations, and hierarchical multi‐atlas LF with multi‐scale feature representation and label‐specific patch partition (HMAS). Based on our experimental results and quantitative comparison, our methods are promising in the magnetic resonance image segmentation.

Clustering algorithms on imbalanced data using the SMOTE technique for image segmentation

Imbalanced data is a critical problem in machine learning. Most imbalanced dataset consists of one or more classes, called the minority class, which do not have enough number of samples for the recognition. Many traditional classification algorithms are unable to recognize the minority class effectively. Clustering algorithms used for image segmentation may have a high accuracy; however, none of samples in the minority class is classified correctly. In this study, we use three approaches, traditional oversampling technique, traditional undersampling technique, and the Synthetic Minority Over-sampling Technique (SMOTE), to reduce the significant difference of imbalance of the number of samples between the majority classes and the minority classes in the dataset. Fuzzy C-means algorithm (FCM) and Possibilistic Clustering Algorithm (PCA) are used to segment the images in which the samples are generated using above sampling methods. Experimental results are evaluated using the Kappa Coefficient and Confusion matrix. Our evaluation shows that the oversampling, undersampling, and SMOTE techniques can improve the imbalanced image segmentation problem with a higher accuracy[1].

An Image-guided Endoscope System for the Ureter Detection

The ureter injury occasionally happens in the gynecology, abdominal and urinary surgeries. The medical negligence may cause severe problems for the hospital, and mental pressure for the doctors. Furthermore, the serious accident brings painful complications for the patients. Thus, it is necessary to locate the ureter, which is covered by peritoneum and connective tissue, for the assisted surgery. The aim is to detect the ureter position, and avoid iatrogenic ureter injury. In order to indicate the ureter position in surgery, we propose an image-guided endoscope system that has both traditional functions of the endoscope system and the additional function of ureter detection. We design an infrared-based pipe that its shape is similar to the ureteral catheter to mark the ureter, and use the multi-spectral camera that can capture both the visual and infrared light to obtain the endoscopic images. To extract the precise contour of the ureter, we propose a hardware-aided detection method, and a high-efficient segmentation algorithm. The hardware-aided method is used to recognize the kind of the captured images. Then the ureter position is extract by the segmentation algorithm. Before the image segmentation, the image enhancement and denoising algorithms are executed to reduce the noise level of images. The extracted contour of the ureter is fused with visible-light images to generate the endoscopic images highlighting the location of ureter. Experimental results indicate that the proposed system can achieve 83.54% and 88.38% of true positive rate (TPR) and positive predictive value (PPV ) respectively. In addition, the frame rate is about 25 frames per second (f/s), which reaches the real-time performance. We proposed a novel image-guided endoscope system for the ureter detection, and the ureter position can be displayed during the surgery. The proposed system may reduce the ureter injury in surgery, and improve the surgical success rate.

An Augmented Reality Endoscope System for Ureter Position Detection

Iatrogenic injury of ureter in the clinical operation may cause the serious complication and kidney damage. To avoid such a medical accident, it is necessary to provide the ureter position information to the doctor. For the detection of ureter position, an ureter position detection and display system with the augmented ris proposed to detect the ureter that is covered by human tissue. There are two key issues which should be considered in this new system. One is how to detect the covered ureter that cannot be captured by the electronic endoscope and the other is how to display the ureter position that provides stable and high-quality images. Simultaneously, any delayed processing of the system should disturb the surgery. The aided hardware detection method and target detection algorithms are proposed in this system. To mark the ureter position, a surface-lighting plastic optical fiber (POF) with the encoded light-emitting diode (LED) light is used to indicate the ureter position. The monochrome channel filtering algorithm (MCFA) is proposed to locate the ureter region more precisely. The ureter position is extracted using the proposed automatic region growing algorithm (ARGA) that utilizes the statistical information of the monochrome channel for the selection of growing seed point. In addition, according to the pulse signal of encoded light, the recognition of bright and dark frames based on the aided hardware (BDAH) is proposed to expedite the processing speed. Experimental results demonstrate that the proposed endoscope system can identify 92.04% ureter region in average.

Sparse patch-based representation with combined information of atlas for multi-atlas label fusion

To obtain a higher accuracy in the multi-atlas patch-based label fusion method, it is essential to have the accurate similarity measure of selected patches. In this study, the authors propose a new sparse patch-based representation method using a local binary texture (LBT) in the atlas image and atlas label information for the multi-atlas label fusion. In the proposed method, the intensity information in a patch is converted into a LBT which is then combined with the labels of corresponding patches from the atlas to form an atom of a dictionary. The initial labels of target images are estimated through a rough segmentation. The voxel in a patch to be labelled is also constructed as a vector similar to the atom. The voxel vector is then modelled as a sparse linear combination of the atoms in the dictionary. Experimental results on two MR brain data sets demonstrated that the proposed method is efficient in the segmentation which can achieve competitive performance compared with the state-of-the-art methods.