Computer-aided detection of lung nodules using outer surface features

Abstract

Abstract. In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.

Fault detection of fabrics using image processing methods

“…An adjacent pixel with a value of 1 identified in any direction is labeled with the same label number, and is subjected to an adjacency examination in 8 directions. According to decision rule, small sizes can be ignored and unlabeled [22]. In Figure 3(a), the binary image adjacencies are shown, in Figure 3(b) the labeled and unlabeled adjacencies are shown.…”

“…As mentioned before, according to the decision rule, small sizes can be ignored and unlabeled [22], [23]. After Wiener filter implementation, largest area on the binary image is detected.…”

Translating evidence into value for stakeholders

“…Two practical concerns that reduce the value of LDCT are waiting time until results and radiation burden. An improvement strategy may thus be to try and reduce waiting time until results are communicated to people, for example by evaluating CT images using machine learning algorithms [51]. Secondly, the fact that the respondents in our sample seemed quite worried about the radiation burden posed by LDCT (mean weight of 0.13) is interesting because the actual incremental risk due to the radiation induced by LDCT is likely to be small.…”

“…This duration can be reduced by assigning distributions only to specific parameters. An example of when this is relevant is when clinical data is available but decision makers are unable or unwilling to provide criteria weights [9,27,51,66]. Time requirements in Bayesian framework are more demanding because of the assignment of not only probability distributions but also of dependence relations in the form of conditional probabilities.…”

“…The other studies applying the deterministic approach were in emergency management [43] and drinking water systems [44,45]. Four studies in healthcare were categorized as probabilistic [9,23,50,51], of which two focus on benefitrisk assessments [9,51], one on infectious diseases [23] and one on water transport (safety) [50]. Four fuzzy set theory studies considered environmental health issues [52-55], while one considered diagnostics [56].…”