An Automated Nuclei Cells Counting Using Image Processing and Quantum Computing

Abstract

Automated Nuclei Cell Counting is needed in biomedical research and clinical diagnosis traditionally counting manually under a microscope that was time-consuming and prone to error. This paper will outline the QUT system, a system that combines image processing and quantum computation to detect and quantify nuclei within a shorter time. Classical techniques like watershed algorithms and morphological operations are used to reduce noise, enhance contrast and segment microscopic images in order to isolate clustered nuclei in a specific manner. Quantum algorithms increase performance by means of parallel processing and optimization which limits computational complexity by many folds over classical algorithms. This facilitates the effective manipulation of large amounts of biomedical image data in order to extract better features and patterns. The proposed hybrid model is assessed on two publicly available datasets of cell images, which give it a better level of accuracy, less processing time and better scalability compared with the traditional methods. Application QUT can be used to perform nuclei counting in real time, with large throughput, using quantum computing and image processing, in cancer diagnostics, pharmaceutical discovery and in cell biology investigations - quantum-enabled biomedical image analysis.