PET Image Segmentation Based on Gabor Annulus Filtering and Region Growing

Devi, Poonam; Mini, M. G.

doi:10.7763/ijcte.2016.v8.1080

Cited by 6 publications

(1 citation statement)

References 23 publications

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“…The seed region growing method categorized the image data by sampling the greyscale values, color values, and intensities of neighboring pixels of initial seed points. We used the seed region growing method to calculate the volume and region of striatal activity (Figure 4) [25]. The initial region-growing value was the maximum count value within the axial section of the striatum, therefore, the maximum striatal activity volume was able to be obtained by this method.…”

Section: Materials and Methodologymentioning

confidence: 99%

Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging

Hsu

Lin

Chen

et al. 2019

Sensors

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The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

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Section: Materials and Methodologymentioning

confidence: 99%