We propose a novel automated strategy for classification of HEp-2 specimens as Mitotic Spindle (MS) or non-Mitotic Spindle (non-MS), which is important for CAD-based Anti-Nuclear Antibody (ANA) detection, in diagnosis of autoimmune disorders. Our strategy is based on the observation that few MS type cells are present in the image along with some other pattern cells in a MS labeled HEp-2 specimen. Hence, the commonly followed majority rule in classification of non-MS cells cannot be applied in this case. We propose that the decision for classifying a specimen as MS or non-MS is based on a pre-defined threshold value on the number of detected MS cells in a specimen. In literature, such evaluation criteria is not clearly analyzed. We note that the MS cells have a distinct visual characteristic, which enables us to use simplistic features representation using the fusion of Gabor and LM filter banks, followed by the Bag-of-words framework and Support Vector Machine (SVM) classification. The experimental results are shown using I3A contest HEp-2 specimen dataset. We achieve 100% True-positive, 5.55% False-positive and 0.97 F-score at the best threshold value of MS. The novel and clearly defined decision strategy makes our approach a good alternative for detection of MS specimen.