A novel harmonic field based method for femoral head segmentation from challenging CT data My name is Shijian Liu, a Lecture from Fujian University of Technology My hometown is Changsha, Hunan province. And I’ve got my PhD degree there from CSU. Today, I’d like to present our recent work on femoral head segmentation from CT images, and our approach is based on harmonic field theory Reporter : Shijian Liu Ph.D. Fujian University of Technology, Fuzhou, Fujian, China

Hip Joint As you know, hip joint, which consist of femoral head and acetabulum, is a one of the most important joint of human body When disease happens to the joint

Pelvic CT Data CT scanning offers an feasible way to diagnose it. The CT data are a serious of images, as particular slices demonstrated like this. Femoral head segmentation and reconstruction can be used to present patient-specific information efficiently to help the diagnosis.

Challenges Narrow or even disappeared space Disturbances Changing topology … … Due to the narrow or even disappeared space between femoral head and acetabulum, disturbances such as deformity from disease, weak boundaries, and the changing topology of femur, segmenting the entire femur accurately from the hip joint is still a challenging problem.

Existing Methods Region-based Contour-based Model-based Thresholding, Morphology, Region growing, … Over-/Under-Segmentation Contour-based Level Set, Snakes, … Initialization, Designation of the energy functions Model-based SSM, ASM, … Priori model, Landmarks, Training, There are many segmentation methods proposed to segment femur, we divide them into 3 categories. The first one is Region-based methods, as the combination of Thresholding, Morphology, Region growing, … Low level methods, and may led to less satisfied results generally. Another category can be named as Contour-based methods such as Level Set, Snakes, … To enable the evolution of those active contours, initial contour and energy function should be given Model-based methods such as SSM and ASM also have such initialization problems. Namely, it relies heavily on a proper initial shape model and landmarks for training

Our approach Harmonic fields Then I will introduce our approach as harmonic field based method, The harmonic field theory which adopt by us has several nice properties as follows. If the region between the red and blue circle are the region of our interest, And pixels belonging to the two circles are preset as, for examples, 0 for the red one and 1 for another, Then Firstly, the harmonic values for pixels in the ROI will transform smoothly. Namely, the iso-lines are concentric circles. Secondly, it is guaranteed that there won’t be any local extreme other than the preset. Which menas, iso-loop, as depicted by the dash line in the figure will never exist. In the real case, harmonic iso-loops display like this. Therefore, one of them can be selected as the segmentation contour.

Joint space identification Our framework consists of several steps. And the first step is the Joint space identification. We use the Statistical information containing in mask data, it can be done with minimal user interactions. Actually, the joint space act as the ROI discussed before.

Barrier Identification Based on Harmonic Field Then harmonic fields can be computed by assigning a Laplacian matrix Which represents the relationship among different pixels And the preset constraints. Voting strategy is used to select an optimal iso-loop as segmentation contour for femoral head. Other part beside femoral head will be extracted by Region-based method for lacking of difficulty for them. The final result is the combination of such two parts.

Experiments and Results Data Set 40 femurs, 512 by 512, 0.65 * 0.65 * 1.00 mm Segmentation Results Our approach tested 40 femurs with complexity categories. Each of the slices has a resolution of 512 by 512 in pixels. The physical spacing of neighboring voxels is 0.65 * 0.65 * 1.00 mm in CT volume. The proposed approach produced with high-quality results approved by several surgeons. There are four pairs of femurs in CT volume are shown in the Figure.

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