 and when Christians fell under suspicion during the reign of Toyotomi Hideyoshi, the Shiga clan were stripped of their land ownership and the castle was turned over to the Nakagawa clan, under whose watch it would remain for 14 generations until the end of the Edo Period. It was under the Nakagawa clan that the castle really thrived, built up with huge stone walls and expanded with another keep and a palace at the request of Toyotomi. Oka Castle became an enormous and formidable fortress, but ironically saw little military action during the Edo era. In 1871, the new Meiji government ordered the castle decommissioned, which the faithful samurai did, no doubt with a tinge of sadness. By 1874, only the stone fortifications remained, and still remain, exactly as they were left.
Today, you can wander the enormous grounds of the castle ruins much like Taki did. So much of the ruins remain undeveloped that you could literally step over the edge of a parapet to a tragic end if you weren’t paying attention. There are no distracting fences, railings, or even warning signage to keep you from enjoying the grounds as they had been left almost 150 years ago, but for that reason, care must be taken that you don’t do anything foolish.
The lack of barriers means you can sit in solitude at the edge of the former castle, hearing nothing but the sound of the river running a hundred meters below or the wind blowing through the old sakura trees that bloom here every Spring. You can peer down wells that contain the same waters that quenched the thirst of samurai lords for centuries. You can run your fingers over huge stones, some taller than yourself, that make up the kilometers of walls spread across the property.
Near the Ninomaru ruins, a statue of young Taki sits, posed as if admiring the beauty of the scenery before him. Though he was a brilliant composer, admitted to the music academy at the age of 15, he died tragically at the age of 23 after contracting tuberculosis while studying overseas. Still, one can think of nowhere better Taki would like people to remember him, sharing his favorite views of and from Oka Castle where memories created in his childhood helped him immortalize the place in song.
The ruins of Oka Castle sit in the hills right outside Taketa city in Oita Prefecture. Taketa city can be reached most conveniently by rental car, though the local Hohi train line can be used to get you to Bungo-Taketa station, which is about a 25-minute walk from the ruins. An entire day could (and should) be spent exploring both the ruins and the historic castle town of Taketa which has many beautifully preserved buildings from the Edo era and a great deal of history related to the samurai clans and Japan’s hidden Christians of the 17th century.
Quality evaluation of virtual slides using methods based on comparing common image areas

Background There are many scanners of glass slides on the market now. Quality of digital images produced by them may be different and pathologists who examine virtual slides on a monitor may subjectively evaluate it. However, objective comparison of quality of digital slides captured by various devices requires assessment algorithms, which will be automatically executed. Methods In this work such an algorithm is proposed and implemented. It is dedicated for comparing quality of virtual slides which show the same glass slide captured by two or more scanners. In the first step this method looks for the largest corresponding areas in the slides. This task is realized by defining boundaries of tissues and providing the relative scale factor. Then, a certain number of smaller areas, which show the same fragments of both slides, is selected. The chosen fragments are analyzed using Gray Level Co-occurrence Matrix (GLCM). For GLCM matrices some of the Haralick features are calculated, like contrast or entropy. Basing on results for some sample images, features appropriate for quality assessment are chosen. Aggregation of values from all selected fragments allows to compare the quality of images captured by tested devices. Results Described method was tested on two sets of ten virtual slides, acquired by scanning the same set of ten glass slides by two different devices. First set was scanned and digitized using the robotic microscope Axioscope2 (Zeiss) equipped with AxioCam Hrc CCD camera. Second set was scanned by DeskScan (Zeiss) with standard equipment. Before analyzing captured virtual slides, images were stitched and converted using software which utilizes advances in aerial and satellite imaging. The results of the experiment show that calculated quality factors are higher for virtual slides acquired using first mentioned device (Axioscope2 with AxioCam). Conclusions Results of the tests are consistent with opinion of the pathologists who assessed quality of virtual slides captured by these devices. This shows that the method has potential in automatic evaluation of virtual slides’ quality.

Results: Described method was tested on two sets of ten virtual slides, acquired by scanning the same set of ten glass slides by two different devices. First set was scanned and digitized using the robotic microscope Axioscope2 (Zeiss) equipped with AxioCam Hrc CCD camera. Second set was scanned by DeskScan (Zeiss) with standard equipment. Before analyzing captured virtual slides, images were stitched and converted using software which utilizes advances in aerial and satellite imaging. The results of the experiment show that calculated quality factors are higher for virtual slides acquired using first mentioned device (Axioscope2 with AxioCam).
Conclusions: Results of the tests are consistent with opinion of the pathologists who assessed quality of virtual slides captured by these devices. This shows that the method has potential in automatic evaluation of virtual slides' quality.

Background
Virtual slides technology enables pathologists to store slides in computer memory and watch them on a display. However, experts who use virtual slides in their work know that their quality may be different. One of the main factors affecting this quality is a type of device used to capture and digitize slides. Each pathologist may have his or her own opinion about the quality of virtual slides coming from a particular device. The problem is that such assessment is subjective and opinions given by various experts may be different.
In this approach we try to find a method of automatic evaluation of virtual slides' quality, where human factor in the assessment is minimal. Such a method compares quality factors calculated for virtual slides captured by different devices, but showing the same real slide. Running this method for many slides and a few scanners will allow us to say which device captures virtual slides of better quality.

Methods
The main goal of this work is designing, implementing and testing a method which compares quality factors, automatically calculated for corresponding areas of virtual slides.
When designing our method for comparing virtual slides' quality, several criteria were followed. First of them is relatively fast execution. Size of virtual slides in computer memory is usually big, so the method does not process the whole slide, but rather some of its areas. To achieve it, the algorithm follows another criterion: ability to deal with big digital images, often stored in hundreds of single files. The method can merge them and crop according to requested areas. The third criterion is accuracy, which means producing reliable results. This is done by finding and selecting corresponding areas, which are then evaluated. This approach allows to visually examine quality of selected corresponding areas, which is helpful when assessing method's results.
The method is divided into the following main steps: 1. Loading virtual slides. 2. Selecting corresponding areas which quality will be compared.
3. Choosing and displaying corresponding fragments of the selected areas.
4. Calculating and aggregating quality measures for the chosen fragments.
In the first step, when loading virtual slides, we assume that they are stored in sets of graphical files of a specified structure. For the test purpose two formats were supported, dedicated for virtual slides used in the experiment. There are several differences between them, like default file names or presence of metadata XML file. Moreover, one slide may be rotated in reference to the second one. All these differences are taken into account in slide loaders, so the format and orientation of all slides in the application are common. The effect of initial virtual slides loading are thumbnails, which are in fact overviews of the whole slides. They are presented to the user in the application. Once created, thumbnail is saved to the file to increase the speed of loading the same virtual slide for the next time.
Even if two virtual slides are the effect of scanning the same glass slide using different devices, areas covered by the virtual slides may be different. Especially significant may be translations of the image. To solve this problem, in the second step of the method corresponding areas of the slides are selected by the user. Here thumbnails saved in the first step are used and user may interactively select interesting common areas by drawing rectangles which mark them out ( Figure 1). Accuracy in selecting exactly corresponding areas results in more precise and reliable quality evaluation of slides' fragments, because compared areas of virtual slides captured by different devices will be almost the same. Good way of accurate areas selection is finding two characteristic points in virtual slides and drawing rectangles between them in all loaded slides.
Area selected by the user may contain even the whole virtual slide, so processing it thoroughly could take a long time. Therefore, only some fragments of the slides are selected for the further analysis and comparison. This step can be justified by the fact that textures on a single slide are relatively monotonous and comparing quality of its fragments gives a good overview of the whole slide's quality.
Choosing slide's fragments is done automatically by dividing area selected by user into a grid and selecting cells with random horizontal and vertical indices. Always a certain number of cells, defined by user, is chosen (in tests 10 fragments were selected from the total number of 400 cell). Each selected cell can be described by coordinates relative to