# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Benchmarks and Methodology  
All of the experiments were conducted in a controlled environment, measuring latency, i.e., the time needed to transfer medical images from MIDOM server to medical specialist's client application running on a mobile device with the Android operating system. We additionally measured and presented the size of the data that we worked with, in order to calculate appropriate compression rates. Although one could argue that file size and transfer latency are tightly coupled, i.e., proportional, we opted to present both the latency and compression rates, as the implied proportionality does not necessarily hold. Data transfers in conventional networks usually impose a certain amount of packet losses that incur re-transfers and thus further charges, as Internet service providers usually charge for the organic data traffic and not for the successful transfer of data granulated as data  
Figure 4: MIDOM components. packages. Furthermore, we wanted to stress the latency reduction as it presents a major benefit for the users of our system. The experiments were conducted in different simulated network environments nowadays, widely available. Those environments are 3G, Wi-Fi, and LTE whose throughputs are available in Table 1.  
We opted for the environments mentioned earlier to test our system in various environments. 3G was included not only because it is still widely used as a fallback network around the globe, but because one of the aims of our system is to widen medical care to places which lack a range of medical specialists, which includes some underdeveloped parts of the world. By that, we wanted to express the necessity for high-quality worldwide medical care to be as reachable as possible. Wi-Fi and LTE environments are de facto standards, and 5G was not included as its usage is still limited to the relatively developed parts of the world which directly counters our aim of delivering a globally applicable system. As much as we wanted to conduct the experiments in a real-world environment, we opted for a simulated one due to the expenses related to the on-field testing, which further influenced our possibility to consider fading models. However, we argue that that does not present a shortcoming, as additionally incurred re-transmissions when considering fading effects would even more stress the benefit of using our customized compression methods. The measuring environment consisted of a personal computer running the Android emulator (manufactured by Google, Mountain View, USA) with Intel x86 Emulator Accelerator (manufactured by Intel, Santa Clara, USA) emulating a Google Nexus device running the Android operating system. Network Link Conditioner, a tool for simulating different network environments, was used to probe our system with different parameters by limiting download and upload bandwidths and delays. The experiments have been conducted on medical studies in each of the aforementioned network environments and using CBPC2, CBPC, and ZIP compressions, paired with raw image experiments, i.e., without compression. To obtain results as reliable as possible, each measurement was conducted five times for faster networks (Wi-Fi and LTE) and three times for the slower, 3G network. Within each measurement we compressed and transferred each image from the respective datasets, calculated averages, and, finally, after all measurements were completed, we once again calculated average of every measurement. The results for ZIP, CBPC, and CBPC2 in both subsequent Sections 4.2 and 4.3 are displayed as relative percentages to raw data size and latencies measured when transferring raw images. The displayed result was rounded to two decimal places prior to multiplying with 100%. Regarding compression methods, we primarily used CBPC2 and CBPC, both described in Section 2.2, as those two methods present a backbone of our work in general and key to delivering high-quality medical content to end devices.