Incorporating CT prior information in the robust fuzzy C-means algorithm for QSPECT image segmentation

Abstract

Bones are a common site of metastases in a number of cancers including prostate and breast cancer. Assessing response or progression typically relies on planar bone scintigraphy. However, quantitative bone SPECT (BQSPECT) has the potential to provide more accurate assessment. An important component of BQSPECT is segmenting lesions and bones in order to calculate metrics like tumor uptake and metabolic tumor burden. However, due to the poor spatial resolution, noise, and contrast properties of SPECT images, segmentation of bone SPECT images is challenging. In this study, we propose and evaluate a fuzzy C-means (FCM) clustering based semi-automatic segmentation method on quantitative Tc-99m MDP quantitative SPECT/CT. The FCM clustering algorithm has been widely used in medical image segmentation. Yet, the poor resolution and noise properties of SPECT images result in sub-optimal segmentation. We propose to incorporate information from registered CT images, which can be used to segment normal bones quite readily, into the FCM segmentation algorithm. The proposed method modifies the objective function of the robust fuzzy C-means (RFCM) method to include prior information about bone from CT images and spatial information from the SPECT image to allow for simultaneously segmenting lesion and bone in BQSPECT/CT images. The method was evaluated using realistic simulated BQSPECT images. The method and algorithm parameters were evaluated with respect to the dice similarity coefficient (DSC) computed using segmentation results. The effect of the number of iterations used to reconstruct the BQSPECT images was also studied. For the simulated BQSPECT images studied, an average DSC value of 0.75 was obtained for lesions larger than 2 cm3 with the proposed method.