Abstract. Recognition of objects in highly structured surroundings is a challenging task, because the appearance of target objects changes due to fluctuations in their surroundings. This makes the problem highly context dependent. Due to the lack of knowledge about the target class, we also encounter a difficulty delimiting the non-target class. Hence, objects can neither be recognized by their similarity to prototypes of the target class, nor by their similarity to the non-target class. We solve this problem by introducing a transformation that will eliminate the objects from the structured surroundings. Now, the dissimilarity between an object and its surrounding (non-target class) is inferred from the difference between the local image before and after transformation. This forms the basis of the detection and classification of polyps in computed tomography colonography. 95 % of the polyps are detected at the expense of four false positives per scan. 1

Object recognition in highly structured surroundings is a challenging task, because the appearance of target objects changes due to fluctuations in their surroundings. This makes the problem highly context dependent. Due to the lack of knowledge about the target class, we also encounter a difficulty delimiting the non-target class. Hence, objects can neither be recognized by their similarity to prototypes of the target class, nor by their similarity to the non-target class. We solve this problem by introducing a transformation that will eliminate the objects from the structured surroundings. Now, the dissimilarity between an object and its surrounding (non-target class) is inferred from the difference between the local image before and after transformation. This forms the basis of the detection and classification of polyps in computed tomography colonography. 95 % of the polyps are detected at the expense of four false positives per scan. 1