We present a cell image quantification method for image-based drug response prediction from patient-derived glioblastoma cells. Drug response of each person differs at the cellular level. Therefore, quantification of a patient-derived cell phenotype is important in drug response prediction. We performed fluorescence microscopy to understand the features of patient-derived 3D cancer spheroids. A 3D cell culture simulates the in-vivo environment more closely than 2D adherence culture, and thus, allows more accurate cell analysis. Furthermore, it allows assessment of cellular aggregates. Cohesion is an important feature of cancer cells. In this paper, we demonstrate image-based quantification of cellular area, fluorescence intensity, and cohesion. To this end, we first performed image stitching to create an image of each well of the plate with the same environment. This image shows colonies of various sizes and shapes. To automatically detect the colonies, we used an intensity based classification algorithm. The morphological features of each cancer cell colony were measured. Next, we calculated the location correlation of each colony that is appeal of the cell density in the same well environment. Finally, we compared the features for drug-treated and untreated cells. This technique could potentially be applied for drug screening and quantification of the effects of the drugs.