A Method of Quantitative Analysis for Dynamic Cellular Morphological Change

Abstract

In recent years, with the advancement of microscopic imaging technique, cellular morphological analysis has become one of the most important branches of biomedical image processing field. Cell image analysis, an assisting method of clinical information acquisition, has played an important role in facilitating the appropriate diagnosis and treatment against some serious diseases such as cancer, leukemia and etc. Early studies of this field focuses on still image information and mainly concerns either cell counting or the morphological characteristics of cell image. Furthermore, much effort has been devoted to analyzing the morphological change of cell image qualitatively, while less attention has been paid to analyzing it quantitatively which in fact is more effective, objective and accurate. Under this very circumstances, our work concentrates on analyzing the cellular morphological change by means of quantitative measurement, and a new method-circumference radial difference (Rd c) is proposed. The basic idea is to measure the morphological change by estimating the radial differences from 360 different angles. Two sets of lymphocyte video database provided by the cooperation hospital are used to verify the proposed approach. Experimental results show that the proposed (Rd c) measurement can produce reasonably good agreement with subjective judgment by people.

Quantitative characterization for lymphocyte deformation in image sequences

“…Besides, in lymphocyte image sequences, changes of cell region and cell contour give the most direct impression to people. Earlier paper discussed quantitative analysis for cellular morphological change based on cell contour [7],…”

“…On the other side, valuable information obtained from image sequences also plays a significant role in aided diagnosis as it can provide more information than cell shape and cell number in a static image, such as cell deformation, cell motility, and cell movement. For this reason, there has been considerable interest in cell dynamic behavior analysis in cell image sequences [1,3,[5][6][7][8][9][10]. Hendrik et al studied two methods for the measurement of shape changes in microscopic images of lymphoid cells [3].…”

“…Usually, a shape is represented by its bounding contour, or its interior region. Earlier paper discussed quantitative analysis for cellular morphological change based on cell contour [7], while it is found that changes of cell region gives the most direct impression to people too. For binary image after segmentation, cell region in different frames can be assumed as sets of points, which contain the coordinates of cell region pixel points.…”