Flickr takes the sun out of the sunset“Flickr takes the sun out of the sunset” — The picture to the left from Flickr shows the full picture and its square thumbnail, in the inset. Thumbnails like these are generated automatically by Flickr and other photo management systems. They work by taking a portion from the center to make the thumbnail. This works well if the center has the most important subject in the picture. But if the picture is relatively wide or tall, and its main subject is not in the center, as in the example at left, with the sun being to one side, the thumbnail misses it. Looking at this example (Long Beach Sunset) in Flickr, note that the first thumbnail on the Flickr page (top left) is the one for the larger picture (that’s shown on our page with the thumbnail in yellow-outlined inset).

In large mass-production systems like Flickr, automatic thumbnails are unavoidable, and my point is not that they should never be used. Instead, my point is that, on many levels, pictures require more human input than text to make them optimally usable. Pattern recognition — the simple observation that the thumbnail of a picture of a sunset SHOULD CONTAIN THE SUN — is something that the human brain does easily, but this does not come naturally for a computer.

Another sort of problem in automatic production of thumbnails is making a thumbnail by simply reducing the size of the large picture. If the main subject of the picture is relatively small, it is not visible in a small thumbnail.

The picture to the left is from the Hardin Library ContentDM collection. The inset in the upper right shows the thumbnail that’s generated automatically by the system, which does a poor job of showing details of the picture. The lower inset shows a thumbnail made manually, which gives a much more clear view of the central image in the picture.

Cropping of a picture to produce a thumbnail, as done here, takes more subtle human judgement than the case with the Flickr picture in the first example, where the weakness of automatic production is obvious. With cropping, there’s inevitably a trade-off between showing the whole picture in the thumbnail or showing the most important subject of the picture. In cases such as this one from ContentDM, where most all of the detail in the picture will be lost in a small thumbnail, it seems better to focus on a central image that will show up in the thumbnail.

Finally, a few examples from Hardin MD, below, show how we have done cropping to improve the detail in our thumbnails. The thumbnails on the left in each of the three pairs are made by simply reducing the size of the full picture. On the right in each pair are the thumbnails we use, that we have made by cropping the full picture before making the thumbnail.

The biomedical, scientific pictures that we work with in Hardin MD are fairly easy to make thumbnails for, because they generally have a well-defined focus, that’s usually captured well by automatically-generated thumbnails. More artistic, humanities-oriented pictures, such as the ones discussed here from Flickr and ContentDM, however, often have more subtle subjects, that benefit from the human intelligent touch in the production of thumbnails.

University of Utah has long been a pioneer in the digitization of medical visual resources, under the leadership of the Eccles Health Sciences library. Utah is especially notable for the wide variety of its resources, with strong collections in several basic biomedical and clinical areas.

Most of the Eccles digital image collections are listed on the Digital Collections page, although they’re mixed in with resources from other sites around the US, and sometimes difficult to identify as having been developed at Utah. Several of the Utah collections are described below.

NOVEL is the Neuro-Ophthalmology Virtual Education Library. This collaborative effort between Eccles Library and the North American Neuro-Ophthalmology Society (NANOS), brings together 11 collections of visual resources from personal working in the discipline around the US.

NOVEL is the only one of the Utah segments that uses the ContentDM digital collection management system. ContentDM is widely used by libraries in the US for historical/archival subjects, but for some reason it’s rarely used for biomedical or scientific subjects. The NOVEL project is notable because it’s one of the few sites anywhere that does this.

In addition to pictures, some of the collections in NOVEL also have videos. A good example of this is the collection of Shirley H. Wray, from Harvard Medical School — See link below for Nerve Palsy.


WebPath, the Internet Pathology Laboratory for Medical Education, includes over 1900 pictures along with text and tutorials. It was developed by Edward C. Klatt MD in the Pathology Dept at Utah; Klatt is now on the faculty at Florida State University. The heart of the WebPath collection for disease-specific pictures is in the Systematic Pathology section, which has images broken down by organ system.

Notable in the Knowledge Weavers section of the Eccles site is the Dermatology Image Bank, done in collaboration with dermatologist John L. Bezzant. This contains striking dermatologic pictures, which are often found by Google Image Search. Knowledge Weavers also includes well-known sites such as Slice of Life and HEAL.

medicalgenetics_20.JPG Another interesting digital resource at Utah, which is not associated with the library, is pictures from the prominent medical textbook, Medical Genetics (lead author Lynn Jorde, published by Mosby). This site also includes some pictures from WebPath. medicalgenetics_twins_65.JPG