A Computer-Aided Diagnosis for Locating Abnormalities in Bone Scintigraphy by a Fuzzy System With a Three-Step Minimization Approach

Abstract

Bone scintigraphy is an effective method to diagnose bone diseases such as bone tumors. In the scintigraphic images, bone abnormalities are widely scattered on the whole body. Conventionally, radiologists visually check the whole-body images and find the distributed abnormalities based on their expertise. This manual process is time-consuming and it is not unusual to miss some abnormalities. In this paper, a computer-aided diagnosis (CAD) system is proposed to assist radiologists in the diagnosis of bone scintigraphy. The system will provide warning marks and abnormal scores on some locations of the images to direct radiologists' attention toward these locations. A fuzzy system called characteristic-point-based fuzzy inference system (CPFIS) is employed to implement the diagnosis system and three minimizations are used to systematically train the CPFIS. Asymmetry and brightness are chosen as the two inputs to the CPFIS according to radiologists' knowledge. The resulting CAD system is of a small-sized rule base such that the resulting fuzzy rules can be not only easily understood by radiologists, but also matched to and compared with their expert knowledge. The prototype CAD system was tested on 82 abnormal images and 27 normal images. We employed free-response receiver operating characteristics method with the mean number of false positives (FPs) and the sensitivity as performance indexes to evaluate the proposed system. The sensitivity is 91.5% (227 of 248) and the mean number of FPs is 37.3 per image. The high sensitivity and moderate numbers of FP marks per image shows that the proposed method can provide an effective second-reader information to radiologists in the diagnosis of bone scintigraphy.

Reliable Evaluation of Performance Level for Computer-Aided Diagnostic Scheme

“…Improper use of these methods can lead to significant biases in the estimated performance levels. We reviewed all research articles on the development and evaluation of CAD schemes that were published in Medical Physics [15][16][17][18][19][20][21] and IEEE Transactions on Medical Imaging [22][23][24] in 2004. We found that, among the 10 papers we reviewed, nine employed incorrect evaluation methods leading to increased bias and/or variance in the estimated performance levels [9].…”

Recognition of Abnormal Uptake through 123I-mIBG Scintigraphy Entropy for Paediatric Neuroblastoma Identification

“…In fact, 99m Tc and 123 I-mIBG are similar scintigraphic procedures; in both, brightness is the primary tool used for abnormal region identification on an image. In [24], the local maximum of brightness in bones and its asymmetries are used to detect abnormalities in the skeleton in 99m Tc whole-body bone scintigraphies.…”

Fuzziness in Medical Image Processing: Representation and Models

“…Another nuclear images as bone scintigraphy have been used as in [36] for the diagnosis of bone diseases with a small-sized rule base such that the resulting fuzzy rules can be easily understood by radiologists. Also the visualization of nerve fibers where investigated in [4] using fuzzy logic methods that allow better comprehension of the human brain.…”