Bladder cancer is one of the most common cancers in the western world. The diagnosis in Germany
is based on the visual inspection of the bladder. This inspection performed with a cystoscope is a
challenging task as some kinds of abnormal tissues do not differ much in their appearance from their
surrounding healthy tissue. Fluorescence Cystoscopy has the potential to increase the detection rate.
A liquid marker introduced into the bladder in advance of the inspection is concentrated in areas with
high metabolism. Thus these areas appear as bright "glowing". Unfortunately, the fluorescence image
contains besides the glowing of the suspicious lesions no more further visual information like for example
the appearance of the blood vessels. A visual judgment of the lesion as well as a precise treatment
has to be done using white light illumination. Thereby, the spatial information of the lesion provided
by the fluorescence image has to be guessed by the clinical expert. This leads to a time consuming
procedure due to many switches between the modalities and increases the risk of mistreatment. We
introduce an automatic approach, which detects and segments any suspicious lesion in the fluorescence
image automatically once the image was classified as a fluorescence image. The area of the contour
of the detected lesion is transferred to the corresponding white light image and provide the clinical
expert the spatial information of the lesion. The advantage of this approach is, that the clinical expert
gets the spatial and the visual information of the lesion together in one image. This can save time and
decrease the risk of an incomplete removal of a malign lesion.
In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography
might be used to support clinical experts in the diagnosis of images derived from imaging modalities
such as endoscopy. In the recent past, a few first approaches for computer assisted endoscopy have been
presented already. These systems use a video signal as an input that is provided by the endoscopes
video processor. Despite the advent of high-definition systems most standard endoscopy systems today
still provide only analog video signals. These signals consist of interlaced images that can not be used
in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches
known today. But most of them are specializations of some basic approaches. In this paper we present
four basic deinterlacing approaches. We have used a database of non-interlaced images which have been
degraded by artificial interlacing and afterwards processed by these approaches. The database contains
regions of interest (ROI) of clinical relevance for the diagnosis of abnormalities in the esophagus. We
compared the classification rates on these ROIs on the original images and after the deinterlacing. The
results show that the deinterlacing has an impact on the classification rates. The Bobbing approach
and the Motion Compensation approach achieved the best classification results in most cases.
Cancer of the esophagus has the worst prediction of all known cancers in Germany. The early detection of suspicious changes in the esophagus allows therapies that can prevent the cancer. Barrett's esophagus is a premalignant change of the esophagus that is a strong indication for cancer. Therefore there is a big interest to detect Barrett's esophagus as early as possible. The standard examination is done with a videoscope where the physician checks the esophagus for suspicious regions. Once a suspicious region is found, the physician takes a biopsy of that region to get a histological result of it. Besides the traditional white light for the illumination there is a new technology: the so called narrow-band Imaging (NBI). This technology uses a smaller spectrum of the visible light to highlight the scene captured by the videoscope. Medical studies indicate that the use of NBI instead of white light can increase the rate of correct diagnoses of a physician. In the future, Computer-Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support the physician in the diagnosis of different lesions in the esophagus. A knowledge-based system which uses a database is a possible solution for this task. For our work we have collected NBI images containing 326 Regions of Interest (ROI) of three typical classes: epithelium, cardia mucosa and Barrett's esophagus. We then used standard texture analysis features like those proposed by Haralick, Chen, Gabor and Unser to extract features from every ROI. The performance of the classification was evaluated with a classifier using the leaving-one-out sampling. The best result that was achieved is an accuracy of 92% for all classes and an accuracy of 76% for Barrett's esophagus. These results show that the NBI technology can provide a good diagnosis support when used in a CAD system.