Ghani M.U.Kanik S.D.Argunşah A.O.Israely I.Ünay D.Çetin M.2023-06-162023-06-1620169.78E+12https://doi.org/10.1109/SIU.2016.7495955https://hdl.handle.net/20.500.14365/359824th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- 122605Dendritic spines, membranous protrusions of neurons, are one of the few prominent characteristics of neurons. Their shapes change with variations in neuron activity. Spine shape analysis plays a significant role in inferring the inherent relationship between neuron activity and spine morphology variations. First step towards integrating rich shape information is to classify spines into four shape classes reported in literature. This analysis is currently performed manually due to the deficiency of fully automated and reliable tools, which is a time intensive task with subjective results. Availability of automated analysis tools can expedite the analysis process. In this paper, we compare ?1-norm-based sparse representation based classification approach to the least squares method, and the ?2-norm method for dendritic spine classification as well as to a morphological feature-based approach. On a dataset of 242 automatically segmented stubby and mushroom spines, ?1 representation with non-negativity constraint resulted in classification accuracy of 88.02%, which is the highest performance among the techniques considered here. © 2016 IEEE.trinfo:eu-repo/semantics/closedAccessClassificationDendritic Spinesleast-squaresNeuroimagingSparse Representation?1?2Classification (of information)Least squares approximationsNeuroimagingSignal processingClassification accuracyDendritic spineLeast SquareLeast squares methodsMorphological featuresNon-negativity constraintsSparse representationSparse representation based classificationsNeuronsConference Object10.1109/SIU.2016.74959552-s2.0-84982793002