An accurate skull stripping method based on simplex meshes and histogram analysis for magnetic resonance images.

Skull stripping methods are designed to eliminate the non-brain tissue in magnetic resonance (MR) brain images. Removal of non-brain tissues is a fundamental step in enabling the processing of brain MR images. The aim of this study is to develop an automatic accurate skull stripping method based on deformable models and histogram analysis. A rough-segmentation step is used to find the optimal starting point for the deformation and is based on thresholds and morphological operators. Thresholds are computed using comparisons with an atlas, and modeling by Gaussians. The deformable model is based on a simplex mesh and its deformation is controlled by the image local gray levels and the information obtained on the gray level modeling of the rough-segmentation. Our Simplex Mesh and Histogram Analysis Skull Stripping (SMHASS) method was tested on the following international databases commonly used in scientific articles: BrainWeb, Internet Brain Segmentation Repository (IBSR), and Segmentation Validation Engine (SVE). A comparison was performed against three of the best skull stripping methods previously published: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), and Hybrid Watershed Algorithm (HWA). Performance was measured using the Jaccard index (J) and Dice coefficient (κ). Our method showed the best performance and differences were statistically significant (p<0.05): J=0.904 and κ=0.950 on BrainWeb; J=0.905 and κ=0.950 on IBSR; J=0.946 and κ=0.972 on SVE.

Registration of renal SPECT and 2.5D US images.

Image registration is the process of transforming different image data sets of an object into the same coordinate system. This is a relevant task in the field of medical imaging; one of its objectives is to combine information from different imaging modalities. The main goal of this study is the registration of renal SPECT (Single Photon Emission Computerized Tomography) images and a sparse set of ultrasound slices (2.5D US), combining functional and anatomical information. Registration is performed after kidney segmentation in both image types. The SPECT segmentation is achieved using a deformable model based on a simplex mesh. The 2.5D US image segmentation is carried out in each of the 2D slices employing a deformable contour and Gabor filters to capture multi-scale image features. Moreover, a renal medulla detection method was developed to improve the US segmentation. A nonlinear optimization algorithm is used for the registration. In this process, movements caused by patient breathing during US image acquisition are also corrected. Only a few reports describe registration between SPECT images and a sparse set of US slices of the kidney, and they usually employ an optical localizer, unlike our method, that performs movement correction using information only from the SPECT and US images. Moreover, it does not require simultaneous acquisition of both image types. The registration method and both segmentations were evaluated separately. The SPECT segmentation was evaluated qualitatively by medical experts, obtaining a score of 5 over a scale from 1 to 5, where 5 represents a perfect segmentation. The 2.5D US segmentation was evaluated quantitatively, by comparing our method with an expert manual segmentation, and obtaining an average error of 3.3mm. The registration was evaluated quantitatively and qualitatively. Quantitatively the distance between the manual segmentation of the US images and the model extracted from the SPECT image was measured, obtaining an average distance of 1.07 pixels on 7 exams. The qualitative evaluation was carried out by a group of physicians who assessed the perceived clinical usefulness of the image registration, rating each registration on a scale from 1 to 5. The average score obtained was 4.1, i.e. relevantly useful for medical purposes.