Numerous studies have shown that the morphological phenotype of a cell or organoid correlates with its susceptibility to anti-cancer agents. However, traditional methods of measuring phenotype rely on spatial metrics such as area, volume, perimeter, and signal intensity, which work but are limited. These approaches cannot measure many crucial features of spatial context, such as chirality, which is the property of having left- and right-handedness. Volume cannot register chirality because the left shoe and right shoe hold the harbor the same amount of volume. Though spatial context in the form of chirality, direction of gravity, and the axis of polarity are intuitive notions to humans, traditional metrics relied on by cell biologists, pathologists, radiologists, and machine learning scientists up to this point cannot register these fundamental notions. The Linearized Compressed Polar Coordinates (LCPC) Transform is a novel algorithm that can capture spatial context unlike any other metric. The LCPC Transform translates a two-dimensional (2D) contour into a discrete sinusoid wave via overlaying a grid system that tracks points of intersection between the contour and the grid lines. It turns the contour into a series of sequential pairs of discrete coordinates, with the independent coordinate (x-coordinate) being consecutive positions in 2D space. Each dependent coordinate (y-coordinate) consists of the distance, between an intersection of the contour and gridline, to the origin of the grid system. In the form of a discrete sinusoid wave, the Fast Fourier Transform is then applied to the data. In this way, the shape of cells in 2D and 3D cell culture, are represented systematically and multidimensionally, allowing for robust quantitative stratification that will reveal in