Automatic dendritic spine detection using multiscale dot enhancement filters and sift features
Rada, Lavdie and Erdil, Ertunç and Argunşah, Ali Özgür and Ünay, Devrim and Çetin, Müjdat (2014) Automatic dendritic spine detection using multiscale dot enhancement filters and sift features. In: IEEE International Conference on Image Processing (ICIP 2014), Paris, France
Official URL: http://spines.bahcesehir.edu.tr/rada_icip2014.pdf
Statistical characterization of morphological changes of dendritic spines is becoming of crucial interest in the field of neurobiology. Automatic detection and segmentation of dendritic spines promises significant reductions on the time spent by the scientists and reduces the subjectivity concerns. In this paper, we present two approaches for automated detection of dendritic spines in 2-photon laser scanning microscopy (2pLSM) images. The first method combines the idea of dot enhancement filters with information from the dendritic skeleton. The second method learns an SVM classifier by utilizing some pre-labeled SIFT feature descriptors and uses the classifier to detect dendritic spines in new images. For the segmentation of detected spines, we employ a watershed-variational segmentation algorithm. We evaluate the proposed approaches by comparing with manual segmentations of domain experts and the results of a noncommercial software, NeuronIQ. Our methods produce promising detection rate with high segmentation accuracy thus can serve as a useful tool for spine analysis.
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