Each of these techniques has a known drawback when segmenting touching cells. Attempts to automate this process of cell characterization has attracted several techniques of cell segmentation, such as the the K −means, fuzzy threshold method, and level set methods. Other mechanical limitations such as low resolution (imaging at high resolution can be very costly in terms of data size) of the imaging device also make it more challenging as it results in poorly defined boundaries and contrast variation within cell bodies. This is even more challenging for 3D image data, due to the different morphology of the cells, for example, cell boundaries across the third (z-) dimension. One major challenge arises from the fact that cells may appear in different shapes and sizes, and are often clumped together in colonies making it very difficult to segment an image data of cells. Researchers in Biology and Chemistry are often interested in which genes are expressed in each cell of an organism, as well as the physical properties of cells, such as volume and density ratio of proteins within the cells. One such area is transcription of gene expression in a cell. Image segmentation is a very important aspect of image processing, and plays vital roles in scientific research.

We compare with some popular approaches and show the advantage of the proposed method. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners.

Image segmentation is an important process that separates objects from the background and also from each other.

Cellprofiler intensity segmentation