Skin Segmentation, Skull Segmentation, and Mesh Generation Tool of Medical Image

Eric Yogi Tjandra(1*), Liliana -(2),

(1) Program Studi Teknik Informatika
(2) Program Studi Teknik Informatika
(*) Corresponding Author


The development of information and computer technology (ICT) has reached to various fields in the world, one of them is medical field. There are new methods of medication and diagnoses currently based on ICT, like MRI and CT Scanner. Both MRI and CT produces data as volume image which contains the scan results of internal organs, also known as DICOM (Digital Imaging and Communications in Medicine) Image. For various needs in the medical world, DICOM image processing is necessary.
This thesis aims to make a feature to load a volume image and do skin and skull segmentation in a short time, and also to do mesh generation from processed DICOM images. Skin segmentation is done by thresholding the image, extracting the largest connected component, and holefilling to fill the unnecessary holes. As for the skull segmentation, the process is done by removing the spines which is connected to the skull, and then extracting the largest connected component. Afterwards, mesh generation is done to produce the 3D objects from the processed images. This mesh generation process is done using the marching cubes algorithm.
The testing results show that the skin and skull segmentation process will work well when there are no other objects that are connected to the skin or the skull. Skin segmentation process takes a significant amount of time, primarily caused by the holefilling process. The time required for mesh regeneration depends on the complexities of the image. The mesh generation result’s quality is affected by resolution reduction ratio, relaxation factor and iteration of smoothing.


Medical Image, Segmentation, Largest Connected Component

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