Development of an improved CAD scheme for automated detection of lung nodules in digital chest images

Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital chest images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference image (which corresponds to the subtraction of a signal-enhanced image and a signal-suppressed image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per chest image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one chest image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.     

Neural networks for the analysis of small pulmonary nodules

PURPOSE: Small pulmonary nodules can be readily detected by computed tomography (CT). The goal of this detection is to diagnose early lung cancer as the five year survival at this early stage is over 70% in contradistinction to the overall 5-year survival of around 10%. Critical to the efficacy of CT for early lung cancer detection is the ability to distinguish between benign and malignant nodules. We explored the usefulness of neural networks (NNs) to help in this differentiation. METHODS: CT images of 28 pulmonary nodules, 14 benign and 14 malignant, each having a diameter less than 3 cm were selected. All were sufficiently malignant in appearance to require needle biopsy and surgery. The statistical-multiple object detection and location system (S-MODALS) NN technique developed for automatic target recognition (ATR) was used to differentiate between these benign and malignant nodules. RESULTS: S-MODALS was able to correctly identify all but three benign nodules. S-MODALS classified a nodule as malignant because it looked similar to other malignant nodules. It identified the most similar nodules to display them to the radiologist. The specific features of the nodule that determined its classification were also shown, so that S-MODALS is not simply a "black box" technique but gives insight into the NN diagnostics. CONCLUSION: This initial evaluation of S-MODALS NNs using pulmonary nodules whose CT features were very suspicious for lung cancer demonstrated the potential to reduce the number of biopsies without missing malignant nodules. S-MODALS performed well, but additional optimization of the techniques specifically for CT images would further enhance its performance.     

Computerized detection of pulmonary nodules in computed tomography images

RATIONALE AND OBJECTIVES: Interpretation of computed tomographic (CT) scans of the lungs is a time-consuming task that involves visual correlation of possible nodules in one section with those in contiguous sections to distinguish actual nodules from blood vessels. Thus, the authors are developing automated methods to detect nodules on CT images of the thorax. METHODS: The computerized technique uses various computer-vision techniques and a priori information of the morphologic characteristics of pulmonary nodules. In each section, the external thoracic wall and lung boundaries are detected, and the features within the lung boundaries are subjected to gray-level thresholding operations. By analyzing the relationships between features arising at different threshold levels with respect to their shape, size, and location, each feature is assigned a likelihood of being a nodule or a vessel. Features in adjacent sections are compared to resolve ambiguous features. Detected nodule candidates are displayed in three dimensions within the lung. RESULTS: The system provided a sensitivity of 94% for nodule detection and an average of 1.25 false-positive results per case. CONCLUSIONS: Continued development of an automated method for detecting pulmonary nodules in CT scans is expected to aid radiologists in the task of locating nodules in three dimensions.     

Neuropathologic diagnosis of Alzheimer disease: interrater reliability in the assessment of senile plaques and neurofibrillary tangles

The potential advantages of using digital techniques instead of film-based radiography have been discussed extensively for the past 10 years. A major future application of digital techniques is computer-assisted diagnosis: the use of computer techniques to assist the radiologist in the diagnostic process. One aspect of this assistance is computer-assisted detection. The detection of small lung nodule has been recognized as a clinically difficult task for many years. Most of the literature has indicated that the rate for finding lung nodules (size range from 3 mm to 15 mm) is only approximately 65%, in those cases in which the undetected nodules could be found retrospectively. In recent published research, image processing techniques, such as thresholding and morphological analysis, have been used to enhance true-positive detection. However, these methods still produce many false-positive detections. We have been investigating the use of neural networks to distinguish true-positives nodule detections among those areas of interest that are generated from a signal enhanced image. The initial results show that the trained neural networks program can increase true-positive detections and moderately reduce the number of false-positive detections. The program reported here can perform three modes of lung nodule detection: thresholding, profile matching analysis, and neural network. This program is fully automatic and has been implemented in a DEC 5000/200 (Digital Equipment Corp, Maynard, MA) workstation. The total processing time for all three methods is less than 35 seconds. In this report, key image processing techniques and neural network for the lung nodule detection are described and the results of this initial study are reported.     

Computerized analysis of interstitial disease in chest radiographs: improvement of geometric-pattern feature analysis

We have been developing automated computerized schemes to assist radiologists in interpreting chest radiographs for interstitial disease based on texture analysis and geometric-pattern feature analysis. In this study, we attempted to improve the performance of the geometric-pattern feature analysis, because the current classification performance with geometric-pattern feature analysis is considerably lower than that of texture analysis. In order to improve the performance in distinguishing between normal lungs and abnormal lungs with interstitial disease, we attempted to remove rib edges in regions of interest (ROIs) by using an edge detection technique, and also to reduce false positives by using feature analysis techniques. In addition, the effects of many parameters on classification performance were investigated to identify proper threshold levels, and subsequently the specificity of the geometric-pattern feature analysis was improved from 69.5% to 86.1% at a sensitivity of 95.0%. Using a combined rule-based method with texture analysis and geometric-pattern feature analysis plus the artificial neural network (ANN) method for classification, a high specificity of 96.1% was obtained at a sensitivity of 95.0%.     

Clinical evaluation of single-exposure dual-energy subtraction chest radiography: with FCR 9501 ES]

Using the one-shot energy subtraction method, we took chest radiography of 1,031 outpatients of Kanazawa University Hospital. Chest radiographs were taken with the FCR 9501 ES system, with patients upright. We studied the clinical efficacy of this diagnostic method. The method and usage procedures involved have been sufficiently technologically refined to permit handling of an adequate number of patients in a reasonable time at a permissible exposure dose. However, the system can be further improved technologically in order to obtain fully consistent high-quality images, regardless of the patient's physique. For well-rounded image reading, three kinds of images must be generated simultaneously: an original image; soft tissue (bone subtracted image) and bone (soft tissue subtracted image) Some soft tissue image were helpful for detecting non-calcified parenchymal nodules that overlapped the rib shadow, and some bone image served to precisely diagnose the existence of abnormal ribs, calcified lymph nodes, and calcified nodules. Furthermore, the bone images were helpful for revealing that the fine infiltrative shadows in the lung fields in original images were actually pleural calcifications and that the poorly defined nodular opacities were, in reality, abnormal rib calcifications. In light of the above, we wish to stress the importance of clinical efficacy of not only soft tissue images but bone images derived using the energy subtraction method. The clinical usefulness of this method was confirmed in 66 of 1,031 patients (6.4%) who had initially been scheduled for routine chest radiography. This result indicates how frequently the method encounters applicable cases when applied non-selectively to general patients who require radiographic chest examination. The advantages of this method will become much more evident with an examination protocol that minimizes unnecessary special examinations or optimizes the balance between screening and special examinations.     

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INTRODUCTION: High-resolution computed tomography (HRCT) with iodinated contrast material has been used by many authors to study solitary pulmonary nodules (SPNs). The degree of enhancement was correlated with the nodule malignancy. MATERIAL AND METHODS: Forty adult patients were examined, before and after contrast agent administration, with incremental dynamic CT. We selected 22 patients with SPNs (3-30 mm phi, except one with 40 mm phi). The CT numbers of the inner nodule were calculated before and 1, 2 and 3 minutes after the i.v. administration of a weight-related dose (1.5 mL/kg/min) of nonionic iodinated contrast agent. A dose of 100 mL contrast agent was used in the first 6 patients. The difference in CT numbers between unenhanced images and the images with maximum enhancement (max. attenuation) was also calculated. RESULTS: Histologic diagnoses included 4 tuberculomas, 3 hamartomas and 15 malignant tumors (9 adenocarcinomas, 5 squamous cell carcinomas and 1 non-Hodgkin lymphoma). The CT numbers (in Hounsfield units, HU) of malignant nodules ranged 12-31 HU (mean: 21.5 HU) before contrast agent administration; the "long-standing" tuberculomas ranged 11-22 HU (mean: 16.5 HU) and the hamartomas had a mean density of 10.5 HU. We excluded for the study two "fresh" tuberculomas, one of which was surrounded by a low-attenuation infiltrate (the halo sign). We selected a threshold value of 20 HU on enhanced CT images to distinguish malignant (> or = 20 HU) from benign (< or = 20 HU) nodules. All lung cancers had complete enhancement (mean density: 35.5 HU). With 20 HU as the threshold value for a positive test, sensitivity was 100%, specificity 85.7%, positive predictive value 93.8% and negative predictive value 100%; test bias was 1.067. CONCLUSION: Positron emission tomography (PET) with 2-[fluorine-18] fluoro-2-deoxy-D glucose is reported to be as accurate as enhanced HRCT, but it does not provide accurate morphological information, is not widely available and it is quite expensive: therefore, in our opinion, CT should be preferred. After examining over 100 patients, we may use our results in the decision analysis comparing surgical risk with cancer risk.     

Improved parameters for unsharp mask filtering of digital chest radiographs

Observer performance with four unsharp mask filtering algorithms for storage phosphor chest radiographs was compared with that with conventional screen-film radiographs in the detection of three types of simulated lung disease: nodules, fine lines, and micronodule clusters. Previously studied parameter sets (small [1.4-mm] and medium [5-mm] filter masks) and two new parameter sets (large [2.5-cm] and ultralarge [7-cm] masks) were compared by means of receiver operating characteristic analysis. With medium and small masks, nodule detection was inferior to that achieved with other modes. Use of ultralarge masks decreased the detection of lines compared with detection with conventional screen-film radiographs. Although detection of micronodule clusters was worse with digital images than with conventional screen-film radiographs, results with large and ultralarge masks were better than with small masks. Overall, filtering with large masks was best suited for simultaneously matching linear, nodular, and micronodular structures. These results suggest that lesion-specific processing of digital chest images is not necessary.     

Solitary thyroid nodule in Khartoum

Ninety four patients with solitary and significantly dominant thyroid nodules were studied. Inadequate sampling was reported in 5 patients (5.9%). In the remaining 89 patients, 64 had simple goitre (72%), 12 follicular adenoma (13.5%), 12 were malignant nodules (6 follicular, 5 papillary and one anaplastic) (13.5%) and one patient had Hashimoto's thyroiditis. The incidence of malignancy in the clinically solitary nodules was 14% (10/70) compared to 10% (2/19) for the dominant nodules which increased to 16% (8/49) versus 10% (4/40) respectively after ultrasonography and histopathological confirmation of the nature of the gland. There is a low positive predictive value for follicular neoplasm with fine needle aspiration cytology (FNAC) (44%). This is due to the fact that reports of suspicious follicular pattern is agreed not to imply definite malignancy, however total lobectomy was done for those patients whereas terms like "not suspicious" and "highly suspicious" are considered to mean, benign and malignant respectively and in those latter groups the prediction was 100%. The study stresses the importance of FNAC techniques in reducing the surgical load with minimal false negative results, increasing the yield of carcinoma and projecting the importance of a significantly dominant nodule in endemic areas.     

Performance of various X-ray film/screen systems in demonstrating small simulated low-density lung nodules

A high-quality, conventional chest radiograph should be obtained in lung cancer screening programs to efficiently detect the faint, small nodular shadows due to early lung cancer in the lung fields, although, it is difficult to image the entire lung field within the linear part of the characteristic curve of the screen/film system owing to the wide variation in tissue density in the thorax, which ranges from the well aerated lung superimposed on the intercostal spaces to the lung area superimposed on the heart or diaphragm. In the detection of early cancer in the lung fields, it is important to consider the very low density (CT values of nearly -600 HU to -300 HU) commonly exhibited by early adenocarcinomas in the lung. The detectability of such nodules would be greatly influenced by the contrast and noise characteristics of the photographic system, the complexity of anatomical structures around the nodule, the characteristics of the observation system for x-ray films, the interpreter, and so on. The choice of x-ray film/screen system basically determines the efficacy of screening; specifically, the contrast characteristics of x-ray films together with noise level, which varies at different optical densities, affect the detectability of nodular shadows. The present study was carried out to determine the most suitable sensitometric characteristics and to assess the effects of the noise level of the x-ray film/screen system on the detectability of nodular shadows within the lung fields, particularly in low-density areas.     

Depot medroxyprogesterone acetate or oral contraception in postpartum adolescents

The purpose of the study was the evaluation of low-dose spiral CT in the detection and assessment of contours of pulmonary nodules. In a prospective investigation 71 consecutive chest CT examinations were acquired both at 30 and 200 mA. Films were interpreted independently by two radiologists. According to the size, nodules were divided into four categories: 10 mm; nodule shape was registered. With both protocols, 240 nodules were detected. The correlation coefficient for both methods was 0.89. Discrepancies were found most frequently in nodules near to pulmonary vessels. Nodule size estimation did not differ more than one size category. Eight spiculated nodules were identified by both techniques. Low-dose spiral CT of the chest has a high sensitivity in the detection of pulmonary nodules. If clinical circumstances require dose minimization, low-dose spiral CT may be advocated as an alternative screening method to conventional dose spiral CT.     

Genetic characterization of an oral treponeme isolated from human subgingival plaque

INTRODUCTION: Since many benign and malignant pathologic conditions can appear as solitary pulmonary nodules, to establish nodule nature is always necessary for correct patient management. Recently, some authors have demonstrated the effectiveness of incremental dynamic CT in distinguishing cancerous from noncancerous lesions. The purpose of this work is to report our personal experience in this field. MATERIAL AND METHODS: We reviewed the incremental dynamic CT scans of 21 patients with a solitary pulmonary nodule < 3.5 cm phi without any calcifications, cavities and fat--namely, 15 carcinomas, 3 granulomas, 2 hamartomas, 1 abscess. Lesion density was evaluated before and 30 s, 1, 2, 3 and 5 min after contrast agent administration; we used a circular region of interest consisting of the central portion of the nodule in all cases and of 60-70% of its area in most cases. We subdivided the nodules into two groups, according to their enhancement: the nodules with > 20 HU and those with < 20 HU. All the lesions were submitted to surgery and histologic studies. RESULTS: Fourteen of 15 carcinomas and one hamartoma had contrast enhancement > 20 HU; an abscess exhibited marked ring-shaped contrast enhancement (positive predictive value: 87%). One carcinoma, three granulomas and one hamartoma had no contrast enhancement, or else it was < 20 HU (negative predictive value: 80%). DISCUSSION: Recently, some authors have demonstrated that malignant nodules, studied with incremental dynamic CT, have higher contrast enhancement than benign nodules. A value > 20 HU is a good predictor of malignancy (positive predictive value: 90%) and, conversely, a value < 20 HU is an unquestionable sign of benignity (negative predictive value: 100%). Our findings confirm the positive predictive value of enhancement > 20 HU, but not its negative predictive value because we found a malignant nodule without contrast enhancement. CONCLUSIONS: Incremental dynamic CT is an effective indicator of solitary pulmonary nodule nature, but its predictive value is not absolute and therefore this technique should be integrated with biopsy in the cases which are clinically or radiologically suspicious.     

Telomerase activity in the differential diagnosis of papillary carcinoma of the thyroid

BACKGROUND: Although fine-needle aspiration (FNA) is 90% sensitive in the detection of papillary carcinoma (PC) of the thyroid, its specificity has been reported as low as 52%. Consequently, patients who have an FNA suspicious for PC may undergo operation for a benign process. The ribonucleoprotein telomerase has been noted to be activated in a wide variety of carcinomas. We examined 30 PCs for telomerase activity to determine whether this would be a useful adjunct to FNA in the diagnosis of lesions suspicious for PC. METHODS: Standard telomere repeat amplification protocol assays were performed on fresh frozen tissue samples from 30 PCs, 3 benign nodules, and 10 normal thyroids. RESULTS: Telomerase activity was documented in 20 of 30 (67%) of the PCs, 0 of 3 benign nodules, and 0 of 10 normal thyroids. In all, 11 of the 20 PCs had FNA cytology that was nondiagnostic of PC, and 2 of the benign nodules had FNA that was suspicious for PC. CONCLUSIONS: The telomerase assay appears useful in the distinction of benign from malignant thyroid lesions that have FNA suspicious for but not diagnostic of PC. On the basis of these findings, a prospective trial examining telomerase activity in FNAs suspicious for thyroid cancer has been initiated.     

Hürthle cell tumor dwelling in hot thyroid nodules: preoperative detection with technetium-99m-MIBI dual-phase scintigraphy

Single injection dual-phase scintigraphy (early and late acquisitions) with 99mTc-MIBI was used to differentiate benign and malignant hot thyroid nodules. METHODS: Thirteen euthyroid and two hyperthyroid patients displaying a hot thyroid nodule on the 99mTc scan due to an autonomously functioning thyroid nodule (AFTN) underwent early (15-30 min) and late (3-4 hr) thyroid scintigraphy after the administration of 740-1000 MBq 99mTc-MIBI. Visual scoring was done to assess nodular tracer uptake and retention. In addition, the nodular-to-thyroid (N/T) uptake ratio in the early and late image and the washout rates (WO) from the nodule and thyroidal tissue were measured. All patients underwent thyroid surgery. RESULTS: Histopathology revealed a Hürthle cell tumor in three nodules, a benign adenoma with oxyphilic metaplasia in two nodules and a benign adenoma without oxyphilic cells in the remaining 10 nodules. The Hürthle cell tumor nodules displayed intense and persistent uptake of 99mTc-MIBI (N/T was 2.81 +/- 0.52 and 5.53 +/- 1.06 in early and late images, respectively; WO from the nodule was 12.33 +/- 0.47, WO from the thyroidal tissue was 22.00 +/- 3.56). The benign nodules showed intense uptake in the early image and intense uptake to absent retention in the late image (N/T was 2.94 +/- 1.31 and 1.62 +/- 0.50 in the early and late images, respectively; WO from the nodule was 20.25 +/- 2.92, WO from the thyroidal tissue was 20.33 +/- 2.92). CONCLUSION: Single injection dual-phase 99mTc-MIBI scintigraphy of the thyroid with AFTN can identify nodules as a result of the activity of a Hürthle cell tumor, since these tumors cause intense and persistent tracer uptake in contrast with a benign AFTN.     

Maximum intensity projection images in the detection of simulated pulmonary nodules by spiral CT

This study was undertaken to investigate the use of maximum intensity projection (MIP) images in the detection of pulmonary nodules by spiral CT (SCT). 40 pulmonary nodules of high density were created by endobronchial deployment of 2 and 4 mm beads in the peripheral airways of five anesthetized dogs. Standard SCT was performed with 5 mm collimation, pitch 2 and reconstruction of contiguous slices. MIP images were generated from overlapped slabs of seven consecutive 3 mm slices, reconstructed at 2 mm intervals and acquired at pitch 2. Scans were reviewed by six radiologists. The data were modelled using ordinal logistic regression for repeated measures, and the Wald chi 2 statistic used to test if there was a difference in nodule detection and reader confidence level between the two techniques. MIP imaging increased the odds of nodule detection by 2.18 (p = 0.0002). Reader confidence level for nodule detection was significantly higher with MIP images (p < 0.00001). MIP images improve the detection rate for small high density pulmonary nodules and increase reader confidence level, when compared with standard SCT.     

The use of fine needle aspiration biopsy in the management of thyroid disease

Fine needle aspiration biopsy is now a first line investigation in thyroid disease. The purpose of this study was to evaluate the results of this technique in comparison with routine histopathology. A total of 593 aspirations over a four year period were included. There were 390 (65.7%) solitary nodules, 124 (20.9%) multinodular goiters, 66 (11.1%) diffuse goitres and 13 (2.2%) recurrent post thyroidectomy nodules. Radioisotope scanning in 386 cases showed 325 (84.2%) cold nodules, 54 (14.0%) warm nodules and 7 (1.8%) hot nodules. There were 458 (77.2%) colloid goitres and cysts, 14 cases of thyroiditis (2.2%) and 30 malignancies diagnosed on fine needle aspiration biopsy. In 19 cases (3.2%) a diagnosis of follicular neoplasm and in 29 cases (4.9%) a diagnosis of suspicious aspirate was made. Histological results were available in 176 cases. In 108 cases findings of histology and FNAB were compared with radioisotope scanning. A sensitivity of 92.8% and 42.8%, a specificity of 90.1% and 98.7% and accuracy index of 90.3% and 94.3% was found, when considering suspicious cases alternatively as positives and negatives. Surgery was recommended in all suspicious cases to prevent reduction in sensitivity of the technique. Fine needle aspiration biopsy was found to be a highly effective procedure which can obviate a lot of unnecessary surgery in thyroid lesions.     

Cardiorespiratory responses to acute hypoxemia in the chronically catheterized fetal llama at 0.7-0.9 of gestation

PURPOSE: To compare hard-copy digital chest radiographs obtained with a selenium-based system with wide-latitude asymmetric screen-film radiographs for detection of pulmonary nodules. MATERIALS AND METHODS: Fifty patients undergoing thoracic computed tomography (CT) for suspected pulmonary nodules were recruited to undergo both digital and screen-film posteroanterior (PA) and lateral chest radiography. Three chest radiologists blinded to the CT results independently reviewed each digital and screen-film radiograph, identified each nodule, and graded their confidence for its presence. RESULTS: Seventy-eight pulmonary nodules (mean diameter, 1.5 cm; range, 0.5-3.5 cm; 62 soft tissue, 16 calcified) were identified with CT in 34 patients, while 16 patients had clear lungs. The mean sensitivity for the detection of all nodules by all readers (PA and lateral) was 66% (95% Cl, 54%, 76%) for digital radiographs and 64% (95% Cl, 52%, 74%) for screen-film radiographs. Differences between the two techniques were not statistically significant (P > .05, Student t test). There was no difference in mean false-positive-true-positive ratios (PA, 0.35; lateral, 0.53) or positive predictive values (PA, 74%; lateral, 65%), and no significant difference (P > .05) was seen in mean reader confidence rating. CONCLUSION: In detecting pulmonary nodules, radiologists perform comparably with selenium-based digital and wide-latitude asymmetric screen-film radiographs.     

Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease

The authors have developed an automated computeraided diagnostic (CAD) scheme by using artificial neural networks (ANNs) on quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs for distinguishing between normal and abnormal patterns. For training and testing of the second ANN, the vertical output patterns obtained from the 1st ANN were used for each ROI. The output value of the second ANN was used to distinguish between normal and abnormal ROIs with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a specified threshold level, the image was classified as abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images. The combination of the rule-based method and the third ANN also was applied to the classification between normal and abnormal chest images. The performance of the ANNs was evaluated by means of receiver operating characteristic (ROC) analysis. The average Az value (area under the ROC curve) for distinguishing between normal and abnormal cases was 0.976 +/- 0.012 for 100 chest radiographs that were not used in training of ANNs. The results indicate that the ANN trained with image data can learn some statistical properties associated with interstitial infiltrates in chest radiographs.     

The influence of clinical history on visual search with single and multiple abnormalities

RATIONALE AND OBJECTIVES: Facilitation of detection by clinical history generally has been found with a single abnormality per image but not with multiple abnormalities. Multiple abnormalities per image can occasion a "satisfaction-of-search" effect in which detection of one lesion is reduced in the presence of other distant lesions. Our experiment studied the combined effect of multiple abnormalities and clinical history on accuracy. METHODS: Detection of native lesions was measured 1) with histories suggestive of the native abnormality; 2) with these histories and added simulated pulmonary nodules; and 3) with the same added nodules and histories suggestive of metastatic disease. These conditions also were compared with those of a previous experiment that were similar but included no history. RESULTS: Detection was substantially improved for appropriately prompted abnormalities even in the presence of a pulmonary nodule. In fact, satisfaction of search was not found in the presence of an appropriate history. Detection of unprompted abnormalities was unchanged when prompts indicated other abnormalities actually present. Prompted abnormalities were detected earlier in search. CONCLUSIONS: History appears to direct perceptual resources to the prompted abnormalities, thereby alleviating satisfaction of search. The presence of nodules yielded a small but consistent reduction in total search time for searches involving false responses, suggesting that satisfaction of search may depend more on reduction in search time than had been indicated by previous research.     

Detecting connectedness

Natural visual images are typically composed of multiple objects, which need to be segregated from each other and from the background. The visual system has evolved to capture a great variety of cues that allow a meaningful segmentation of the visual input. One of these cues is connectedness. Connected image regions are likely to belong to a single visual object, whereas disconnected image regions typically belong to different objects. The visual system should therefore be rather proficient in recovering connected image regions. In the present article we will review evidence in favour of an important role of connectedness detection for figure-ground segmentation, and speculate on the physiological mechanisms that allow the visual system to perform this non-trivial task. We argue that biologically plausible feedforward networks are maladapted for the detection of connectedness. It is proposed that neurons that respond to connected image regions are linked by a network of recurrent connections that we call the interaction skeleton. Neurons spread a tag through the interaction skeleton, which labels cells that respond to the same perceptual object. Tag-spreading costs time and is therefore inconsistent with extremely rapid object recognition. We will discuss the pros and cons of two such tags: synchrony and rate modulation.