Automatic Assessment of Ki-67 Proliferation Index in Lymphoma

Background: Haematopathological Ki-67 is used principally to measure the proliferation rate in the assessment and grading of malignancies. Ki-67 is based on a powerful staining method for distinguishing benign from malignant proliferation. The index uses a nuclear protein expression and it has been widely used to evaluate the proliferative activity of lymphoma. The clinical value of Ki-67 includes defining prognosis (among lymphomas), predicting drug response, and setting eligibility criteria for clinical trials. The Ki-67 score or index should be expressed as the percentage of positively stained cells among the total number of invasive cells in the area scored. With the Ki-67 marker, the proliferation fraction of low-grade follicular lymphomas (FLs) is usually less than 20% (as shown here) and that of high-grade FLs is greater than 30% [1]. Manual Ki-67 proliferation assessment is a very time-consuming and operator-dependent task at the same time. Therefore, several studies have examined the use of image analysis software to measure faster the nuclear staining index of Ki-67 in lymphomas. A few studies have focused on the measurement of proliferation index in FLs and found that automated Ki-67 counts were similar to manual counts [2-3]. A major source of difference between automatic and manual Ki-67 scores is the scoring method that depends on the strategy of counting or the estimation and choice of the area to count. Methods: In this research, an automatic unsupervised learning-based system was proposed for accurate and fast Ki-67 scoring in lymphoma. The proposed methods were designed to use image processing tools and detect robustly the positive and negative cells for Ki-67 antibody. The goal of the proposed method was to assess the proliferation index (percentage of Ki-67 positive lymphoma cells) to provide better treatment options for lymphoma patients. The proposed system consisted of the following sections: pre-processing, feature extraction, segmentation, and post-processing (Figure 1). To highlight specific histological structures of Ki-67 stained images such as positives cells (brown color ones), we performed pre-processing such as color transform from RGB space to brown-ratio space. For smoothing and filling the region of each cell on the image, the morphological filling was used. After the pre-processing section, color features, such as the mean of brown-ratio color space and blue channel of RGB image in a 3 × 3 block, were extracted from the