Development of MatLab Coding for Early Detection of Leukemia through Automated Analysis of RBCs

Background: Bone marrow biopsy has become an integral part of leukemia diagnosis and its treatment. Several advancements are being made towards the analysis of digital images of biopsy samples. Recently, the FDA approved the procedures of digital health. In tune with that, digital image analysis has become propelled. With the advent of high-throughput technologies, the scientific community focuses on the red blood cells (RBCs) for early detection of cancer, including leukemia. The reasons are due to their abundance in peripheral blood and hence, easily accessible compared to the bone marrow biopsy procedure. High magnification and high-resolution electron microscopy-based ultra-structural analysis of RBCs already proved the utility of the hypothesis about a decade ago. However, in clinical set-up, electron microscopy-based procedures are the major bottleneck in the implementation of early detection of leukemia. Algorithm-based computer vision may be suitable to overcome this limitation. Methods: An intensive search with PubMed and Google for early diagnosis of leukemia through RBC light microscopic images was made. For this search, the image processing algorithm for RBC was also made in PubMed, IEEE Xplorer and Google; and the latest developments are noted. To fill the existing gap, a user-friendly MatLab coding is developed for automated analysis of RBC images. Result: RBC images from both normal and leukemia were analyzed with the developed code. Each RBC cells were analyzed individually for each sample of normal and leukemia. Therefore, in the output cellular characteristics namely, radius, perimeter, area, convexity and solidity were represented in a quantitative manner. Comparison of mean values between normal and leukemia groups for corresponding variables showed statistical significance. In the test run, data of 82 RBC cells from single leukemia sample when compared with the data of pooled data of normal samples also showed significant difference (P<0.05). Conclusion: Thus, the developed code successfully distinguishes between RBC cells of leukemia and normal. We hope that this RBC based developed code would be useful in identifying earlystage of leukemia in an individual patient; however, for this, ~100 RBC cells are needed to be analyzed. As the developed code is based on RBC based diagnosis therefore, may be applied to other cancers and if needed, further modification is possible due to availability of code. Thus, it is expected that the developed MatLab code may play a role in preventive oncology.