# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
## 1 Introduction  
Many diseases are diagnosed based on the cellular structures in their respective tissue specimens as the cellular structures can provide quantitative information about the diseases and help in the study of disease progression. For example, the density of cell nuclei in histological images is an important feature for automatic breast or skin tumor grading [1]. The difference between normal skin cells and abnormal skin cells can be seen in Figure 1. In human intervened diagnosis procedure, histopathologists typically examine the tissue under a microscope, and the diagnostic accuracy depends on the pathologists' personal experience, which sometimes leads to intra and inter observer variability [2]. To overcome these limitations, several computer-aided diagnosis (CAD) techniques have been proposed in the literature for the diagnosis. Due to a wide variety of nuclei appearances in different organs, and staining procedures, accurate and efficient segmentation of cell nuclei is an important step in most histopathology-based CAD techniques. The detection of cells in a histology image may also be the first step towards cell segmentation.  
Since cell nuclei typically have circular shapes, they can be considered as blob-like structures which can be detected efficiently using scale-space theory. [PERSON] et al. [1] proposed an efficient technique for nuclei detection using directional gLOG (generalized Laplacian of Gaussian) kernels on red channel image of H&E (Hematoxylin and Eosin) strained color histopathology images. The technique generates intermediate response maps using directional gLOG kernels. It is possible to obtain more than one point from different response maps, corresponding to the same nuclei in the input image. Therefore, seeds from these response maps are merged using mean-shift clustering. It gives a promising performance in nuclei seeds detection.  
The histological images typically have a large size. For example, a 20 mm\({}^{2}\) glass slide tissue scanned with a resolution of 0.11625 \(\upmu\)m/pixel (at 40\(\times\) magnification) will consist of about 2.96 \(\times\) 10\({}^{10}\) pixels, and will approximately require 80 GB of storage space in an uncompressed color format (24 bits/pixel) [2]. In addition, gLoG kernels generally require significant computation. As a result, the nuclei detection techniques typically have high computational complexity, and software implementation on general purpose processors (GPP) requires a significant amount of processing time. For real-time diagnosis, it would be helpful to develop a hardware accelerator for faster nuclei detection. With advances in CMOS and fabrication technology, Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU) are being widely used as a High-Performance Computing (HPC) solution to overcome the GPP limitations. GPUs are efficient for data parallel applications with high memory bandwidth requirement and are typically programmed using high-level languages, such as CUDA. On the other hand, FPGAs have more flexibility than GPUs and are efficient for both _data_ and _task_ parallel applications.  
In this paper, we propose an FPGA-based hardware architecture for cell nuclei detection in a histology image obtained using H&E stain. The proposed architecture uses data parallelism. To reduce the computation burden and power consumption, floating point arithmetic is implemented in a fixed-point form without losing much accuracy. The architecture has low latency. The organization of the rest of the paper is as follows. Section 2 gives details on nuclei detection algorithm and its implementation. Section 3 presents experimental results and performance evaluation. Discussion on results is presented in Section 4, followed by conclusion in Section 5.