超音波影像(Ultrasound Image)在醫學影像工具中常扮演著眼睛的角色，可以幫助我們觀察眼睛看不到的身體病變;它具有影像擷取容易、非侵入式、影像及時成像等等優勢，已廣泛應用於各種醫學治療中。另一方面，超音波系統也有其缺點，因為擷取設備的關係，醫療超音波影像有別於一般自然影像處理，其成像結果較不清楚。然而，在醫療超音波影像上，細節資訊表示著可能發生的潛在病變與不尋常狀況，故需要保留以及增強超音波影像中細節紋理以利於醫師判斷病情。本研究提出以Tetrolet轉換，利用其能量集中的特性來保留細節紋理資訊，並加入自適性直方圖等化(Adaptive Histogram Equalization, AHE)，來自動調整超音波影像的對比以達到上述功能。但Tetrolet轉換在VLSI硬體實現時，會有較長的運算複雜度(Complexity)與記憶體(Memory)使用量較大的問題。有鑑於此，本研究提出針對Tetrolet轉換的改良，以降低比對搜尋模板法的速度與記憶體使用量，我們稱之為低複雜度比對模板法(Low-complexity Matching Pattern, LMP)。再利用所提出的LMP並配合查表法(Look-up Table)，來解決AHE在線性內插時所產生硬體運算量過大的問題。由實驗結果得知，改良式架構在運算時間與硬體的提升上，可適合應用於手持式超音波影像系統。最後，我們將其硬體架構實現在Xilinx 7系列的ZedBoard Zynq-7000 ARM/FPGA SoC Development Board上。 An ultrasound image plays the role of eyes in medical imaging tools, and it can help us to observe the physical lesions. The ultrasound image system captures the image more easily than other medical system, and non-invasive detection can reduce the diagnostic time. It is widely used in a variety of medical treatments and medical testing currently. On the other hand, medical ultrasound images are often unclear due to the capturing devices used. As the medical ultrasound images can provide information regarding potential pathological changes and abnormalities, the images must be preserved and enhanced to help doctors to monitor the course of a disease. This research work proposes to use the energy concentration characteristics of Tetrolet transform to preserve the texture information and integrate the technique of adaptive histogram equalization (AHE) to automatically adjust the contrast of an ultrasound image, thereby to resolve the aforementioned function. However, the implementation of the Tetrolet transform in very large scale integration (VLSI) architecture brings issues such as computational complexity and large memory use. In view of these problems, this research work proposes a low-complexity matching pattern (LMP) to reduce the amount of memory used. The LMP is combined with a look-up table approach to solve the issues of the excessive calculations generated in AHE during linear interpolation. The experimental results indicate that the modified algorithm is applicable in hand-held ultrasound imaging systems as it is able to improve calculation times and hardware. Finally, It was implemented in an Xilinx 7 series ZedBoard Zynq-7000 ARM / FPGA SoC Development Board.
