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Cervical cancer is the second most common malignancy among women worldwide, if it is detected in early stage, cure rate is relatively high. Computer aided abnormality detection for cervical smear is developed to assist medical experts to handle the microscopy images, examine cell abnormalities and diagnose dyskaryosis. The microscopy images of cells in cervix uteri are stained by the tumor marker Ki-67, so that the abnormal nuclei present brown while normal ones are bluish. Segmentation is the most important and difficult task to calculate the ratio of abnormal nuclei to all nuclei. In order to achieve accurate segmentation of nuclei, we propose a multi-level segmentation approach for abnormality identification in microscopy images. First level segmentation aims to partition abnormal (stained) nuclei regions and all nuclei regions. Because of under-segmentation after first level segmentation, second level segmentation is applied to further partition the clustered nuclei. In order to classify touching regions of clustered nuclei and separate regions of single nucleus, relevant meaningful features are extracted from regions of interest. Consequently all the nuclei regions are separated and in conjunction with the abnormal nuclei regions in the first level segmentation, the abnormality i.e. ratio of abnormal nuclei to all nuclei is obtained. Experimental results indicate that our method achieved an accuracy of 93.55% and 95.8% in term of abnormal nuclei and all nuclei respectively for identification of abnormalities. Our proposed method produces a satisfactory segmentation.