Fissure Detection Research Articles

Novel locally nebulized indocyanine green for simultaneous identification of tumor margin and intersegmental plane.

Segmentectomy, recommended for early-stage lung cancer or compromised lung function, demands precise tumor detection and intersegmental plane identification. While indocyanine green (ICG) commonly aids in these aspects using near-infrared imaging, its separate administrations through different routes and times can lead to complications and patient anxiety. This study aims to develop a lung-specific delivery method by nebulizing low-dose ICG to targeted lung segments, allowing simultaneous detection of lung tumors and intersegmental planes across diverse animal models. To optimizing the dose of ICG for lung tumor and interlobar fissure detection, different doses of ICG (0.25, 0.1, and 0.05mg/kg) were nebulized to rabbit lung tumor models. The distribution of locally nebulized ICG in targeted segments was studied to evaluate the feasibility of detecting lung tumor and intersegmental planes in canine lung pseudotumor models. Near-infrared fluorescence imaging demonstrated clear visualization of lung tumor margin and interlobar fissure using local nebulization of 0.1mg/kg ICG for only 4min during surgery in the rabbit models. In the canine model, the local nebulization of 0.05mg/kg of ICG into the target segment enabled clear visualization of pseudotumor and intersegmental planes for 30min. This innovative approach achieves a reduction in ICG dose and prolonged the visualization time of the intersegmental plane and effectively eliminates the need for the hurried marking of tumors and intersegmental planes. The authors anticipate that lung-specific delivery of ICG will prove valuable for image-guided limited resection of lung tumors in clinical practice.

Detection of pit and fissure sealant microleakage using quantitative light-induced fluorescence technology: an in vitro study.

This in vitro study aimed to evaluate the feasibility of quantitative light-induced fluorescence (QLF) technology for detecting the presence and severity of microleakage of pit and fissure sealants. The areas of interest (AOIs) were 160 pits and fissures of 40 extracted permanent teeth. Fluorescent images were acquired using a QLF device, and the maximum fluorescence loss ΔFmax of each AOI was analyzed. After staining and cross-sectioning of the teeth, histological dye penetration was scored on a scale of 0 to 3. The relationship between ΔFmax and microleakage depth was analyzed, and the areas under the curve (AUCs) were calculated. The │ΔFmax│ increased as microleakage depth increased. The ΔFmax values of microleakage areas showed a strong significant correlation with the histological scores of dye penetration (r = -0.72, P = 0.001). AUC analysis showed a high diagnostic accuracy for microleakage depth (AUC = 0.83-0.91). The highest AUC of 0.91 was found when differentiating the outer half microleakage of the sealant (histological score 0 vs. 1-3). QLF technology is effective in assessing the presence and severity of microleakage, suggesting its potential for noninvasive detection and monitoring of sealant microleakage in clinical settings.

GFSegNet: A multi-scale segmentation model for mining area ground fissures

Precise identification of ground fissures is of paramount importance for the safety and environmental management of coal mining areas. However, the surface environment in coal mining regions is complex, and, to date, the efficiency of artificial fissure detection has been relatively low. Therefore, we have proposed a ground fissure automatic identification model based on an encoder–decoder architecture known as the Ground Fissure Segmentation Network (GFSegNet). The encoder adopts a deep-shallow decoupled mode. The shallow network achieves spatial and spectral domain interaction by introducing adaptive Fourier convolution. The deep network adopts a hierarchical Transformer with an efficient self-attention mechanism for global modeling of fine-grained semantics. The decoder is designed as a multi-scale feature fusion structure embedded in pyramid pooling modules, aiming to efficiently utilize multi-scale ground fissure information. To advance the application of deep learning in ground fissure identification, we created a coal mining area ground fissure segmentation dataset from drone imagery, known as the mine ground fissure unmanned aerial vehicle dataset (MGF-UAV). On this dataset, the overall performance of GFSegNet surpasses the current leading segmentation models, and its reliability and generalization capabilities are further validated on additional datasets (Crack500, DeepCrack, CrackForest and ISPRS-Postdam). This research has brought expansion and innovation to the field of automatic ground fissure recognition in coal mining areas, offering new perspectives and methodologies for the application of deep learning techniques in this domain.

Application of Ground-Penetrating Radar with the Logging Data Constraint in the Detection of Fractured Rock Mass in Dazu Rock Carvings, Chongqing, China

Geologic interpretation results from conventional ground-penetrating radar (GPR) detection methods tend to have a certain degree of uncertainty. In order to improve the reliability of ground-penetrating radars in the detection of rock mass fissures in grottoes, this study proposes a ground-penetrating radar detection method with the logging data constraint, which is applied to detect the fractured rock mass in the Baodingshan Scenic Area of Dazu Rock Carvings, Chongqing, China. First, conventional logging and borehole televiewer data were compared and verified, yielding detailed lithological and wellbore fissure information. Next, electromagnetic wave velocity was calibrated using GPR profile and the depth of the stratigraphic interface determined by borehole data. Utilizing this calibrated velocity, we are able to accurately calculate the depth values of anomalies in GPR interpretation profiles. Subsequently, we compared the preliminary GPR interpretation profile with the borehole televiewer images. After eliminating false anomalies caused by interference, we obtained more reliable location information for detection targets such as fissures, fracture zones, and weak interlayers. The results of fissure detection in the Dazu Rock Carvings indicate that the detection results of ground-penetrating radar are verified and supplemented under the constraints of stratigraphic and well-wall fissure information obtained by logging. This effectively mitigates the influence of multiplicity and false anomalies of GPR detection on interpretation results. GPR with the logging data constraint enhances the accuracy of the fissure detection results, providing novel technical means for the protection and restoration of grotto relics.

Detection of Imperceptible Intervertebral Disc Fissures in Conventional MRI-An AI Strategy for Improved Diagnostics.

Annular fissures in the intervertebral discs are believed to be closely related to back pain. However, no sensitive non-invasive method exists to detect annular fissures. This study aimed to propose and test a method capable of detecting the presence and position of annular fissures in conventional magnetic resonance (MR) images non-invasively. The method utilizes textural features calculated from conventional MR images combined with attention mapping and artificial intelligence (AI)-based classification models. As ground truth, reference standard computed tomography (CT) discography was used. One hundred twenty-three intervertebral discs in 43 patients were examined with MR imaging followed by discography and CT. The fissure classification model determined the presence of fissures with 100% sensitivity and 97% specificity. Moreover, the true position of the fissures was correctly determined in 90 (87%) of the analyzed discs. Additionally, the proposed method was significantly more accurate at identifying fissures than the conventional radiological high-intensity zone marker. In conclusion, the findings suggest that the proposed method is a promising diagnostic tool to detect annular fissures of importance for back pain and might aid in clinical practice and allow for new non-invasive research related to the presence and position of individual fissures.

Detection of hidden mining-induced ground fissures via unmanned aerial vehicle infrared system and ground-penetrating radar

We propose a new combined detection method for the hidden ground fissures using unmanned aerial vehicle (UAV) with infrared camera and ground-penetrating radar (GPR) and applied it in the panel No. 12203 of the Daliuta coal mine. Subsequently, the extensive UAV aerial photography was taken to obtain the ground surface temperature of the entire longwall panel. The GPR detections along three survey lines in the selected ground surface zone of interest were carried out to find the positions of hidden ground fissures induced by underground mining. Based on the temperature measurement and analysis, the temperature of the surface above the hidden ground fissure is higher than that of the surface in non-fissure zone. The location of the hidden ground fissure can be determined by selecting the zone with a surface temperature higher than the temperature threshold, which is equal to the maximum average temperature of each surface zone. The similar track of hidden fissures can be drawn by GPR detection. The hidden ground fissure, with longitudinal lengths of 1–5 m and staggering horizontal lengths of 1–3.5 m, is mainly distributed 20–50 m in front of the working face, and between the step-like fissures. As the working face advances, the hidden ground fissure is recoverable with a cyclic evolution of formation, longitudinal development, and pinch-out. The optimal period for fissure detection is 9:20 a.m. to 11:30 a.m. at the environmental condition of this study, during which the open fissure has the lowest temperature range compared with the ground surface and vegetation. The results show that the proposed synergistic method can efficiently and accurately locate the hidden ground fissures and explain the distribution mechanism of ground temperature values in the mining area.

MFPA-Net: An efficient deep learning network for automatic ground fissures extraction in UAV images of the coal mining area

Ground fissures caused by high-intensity underground coal mining activities will damage the ecological environment and endanger mine safety. The complicated surface conditions in coal mining areas make fissure detection labor-intensive work, and manual fissure extraction is inefficient, restricting the surface monitoring work. In this study, an efficient Deep Learning (DL) network named MFPA-Net (Multi-scale Feature Pyramids and Attention Network) is proposed to extract ground fissures in Unmanned Aerial Vehicle (UAV) images from the coal mining area automatically and accurately. In MFPA-Net, the Dilated Residual Networks (DRN) are used to extract diverse context information, the Dual Attention Mechanism (DAM) is introduced to integrate the dependence of pixels' spatial location and feature channels to generate high-level features, the Atrous Spatial Pyramid Pooling (ASPP) is utilized for mining multi-scale context information from high-level features, and the Multi-scale Feature Pyramid Network (MFPN) is designed to combine high-level and low-level features. Moreover, the Focal Tversky Loss Function is adopted to handle the unbalanced samples. To promote DL technologies application in the fissure monitoring of mining areas, the GFCMA (Ground Fissures of the Coal Mining Area) dataset is constructed. Experiments on GFCMA show that MFPA-Net can achieve high Precision (69.4%), Recall (70.7%), F1-Score (70.0%), and Mean Intersection over Union (MIoU) (75.1%) simultaneously, which significantly outperform traditional image processing methods and recently DL networks. Experiments on public pavement datasets Crack500 and DeepCrack prove MFPA-Net's high reliability and widespread applicability. The performance of the trained MFPA-Net on real large-scale scenarios demonstrates its practical value, strong robustness, and high efficiency. This study provides a solution for rapid monitoring of ground fissures under complicated surface conditions, which can serve the safe production and ecological restoration high-efficiently in mining areas.

Uncertainty Analysis of Inverse Problem of Resistivity Model in Internal Defects Detection of Buildings

Fissure detection in ancient buildings is of vital importance in the evaluation of resistance or remediation in urban areas. Electrical resistivity imaging is an efficient tool to detect fissures or moisture erosion in buildings by highlighting the resistivity contrasts in the inversion models. The traditional results of ERT images give deterministic interpretations of the internal artifact. However, the existence of equivalent models may correspond to different physical realities in engineering cases, to which the traditional ERT model cannot respond. In this paper, through the application of a field test on an ancient wall, it is shown that the segmentation of the equivalent model family is applicable to solve the internal defects detection problem in a probabilistic approach. It is achieved by performing a probabilistic approach to apply the uncertainty analysis. The procedure begins with the reduction in dimensions of the model by spectral decomposition, and the uncertainty space is rebuilt via Particle Swarm Optimization (PSO). By computing the uncertainty space, probabilistic maps are created to demonstrate the electrical anomaly in a simpler structure. The proposed method provides a more accurate approach for the internal defects detection of buildings by considering the possibilities hidden in the equivalent model family of ERT results.

Automated Extraction of Ground Fissures Due to Coal Mining Subsidence Based on UAV Photogrammetry

Widespread ground fissures caused by coal mining subsidence are a main cause of ecological destruction in coal mining areas, and the rapid monitoring of ground fissures is essential for ecological restoration. Traditional fissure monitoring technologies are time consuming and laborious. Therefore, we developed a method to automatically extract ground fissures from high-resolution UAV images. First, a multiscale Hessian-based enhancement filter was utilized to enhance the ground fissures in grayscale images. Then, a simple single-thresholding operation was applied to segment the enhanced image to generate a binary ground fissure map. Finally, incomplete path opening was performed to eliminate the noises in the fissure extraction results. We selected the N1212 working face of the Ningtiaota Coal Mine in Shenmu County, China, as the study area. The results indicated that the ranges of correctness, completeness, and the kappa coefficient of the extracted results were 66.23–79.00%, 69.03–73.22%, and 67.91–75.88%, respectively. Image resolution is the key factor for successful fissure detection; the method proposed in this paper can extract ground fissures with a width greater than one pixel (2.64 cm), and the detection ratio for fissures with a width greater than two pixels was over 87%. Our research has solved the problem of the rapid monitoring of ground fissures to a certain extent and can act as a valuable tool for ecological restoration in mining areas.

Underground Morphological Detection of Ground Fissures in Collapsible Loess Area Based on Three-Dimensional Laser Scanning Technology

Underground coal mining inevitably causes ground fissures, especially permanent cracks that cannot be closed at the boundary of the working face. Studying the underground three-dimensional morphology of the permanent cracks allows one to accurately constrain the formation and development of the ground fissures. This information will contribute to reducing mine disasters and is also a prerequisites to avoid environmental pollution. We selected the Zhangjiamao coal mine (China), which is situated in a collapsible loess area, as a case study for deciphering the formation of permanent cracks. After injecting gypsum slurry into the mine, a three-dimensional model of the ground fissures is obtained by three-dimensional (3D) laser scanner technology that records the 3D underground morphology. Integrating the geological context of a collapsible loess area, the characteristics and main processes of the ground fissure development are constrained: (1) The width of the ground fissure decreases to 0 with increasing depth and is strongly affected by the soil composition. (2) Along the vertical extension direction, the ground fissures are generally inclined to the inner-side of the working face, but the direction remains uncertain at different depths. (3) The transverse propagation direction of the ground fissure becomes more complex with increasing depth. (4) Under the influence of soil texture and water, loose soil fills the bottom of the ground fissure, thus affecting the underground 3D morphology.

Identification of mining induced ground fissures using UAV and infrared thermal imager: Temperature variation and fissure evolution

The identification and treatment of mining-induced ground fissures are of great significance to mine safety and ecological and environmental protection. In this study, a novel method for ground fissure identification and exploration by infrared remote sensing onboard an unmanned aerial vehicle (UAV) was proposed. Using this method, a region of interest (ROI) that includes ground fissures directly above the middle of a long wall face, No. 12401 in the Shangwan coal mine, was monitored continuously during the day and night. Direct field measurements of ground fissure properties were also conducted to provide a calibration dataset for UAV measurements. Using the direct visible image at 5:00 pm as a reference, the average errors of the length and maximum width of Fissure I obtained from infrared images from 9:00 pm to 5:00 am on the next day, were estimated to be 1.8% and 6.5%, respectively. The diurnal variation of the fissure temperature is sinusoidal, and the range of temperature variation in the fissure decreases with the increase in depth. There is an apparent difference between the two common types of fissures depending on whether the fissure has a direct connection to an aquifer or a goaf. In this study, UAV, infrared thermal imager, and visible light camera data were successfully employed to effectively identify mining-induced ground fissures. In addition, the fissure detection error was validated, and the appropriate time for utilizing this method was obtained. Our results show that to identify the two aforementioned types of fissures, monitoring should be conducted between 3:00 am and 5:00 am. This study lays a foundation for the study and application of UAV and infrared thermal imagers for the identification of ground fissures induced by underground mining in large areas.

Automated Midline Estimation for Symmetry Analysis of Cerebral Hemispheres in FLAIR MRI.

To perform brain asymmetry studies in large neuroimaging archives, reliable and automatic detection of the interhemispheric fissure (IF) is needed to first extract the cerebral hemispheres. The detection of the IF is often referred to as mid-sagittal plane estimation, as this plane separates the two cerebral hemispheres. However, traditional planar estimation techniques fail when the IF presents a curvature caused by existing pathology or a natural phenomenon known as brain torque. As a result, midline estimates can be inaccurate. In this study, a fully unsupervised midline estimation technique is proposed that is comprised of three main stages: head angle correction, control point estimation and midline generation. The control points are estimated using a combination of intensity, texture, gradient, and symmetry-based features. As shown, the proposed method automatically adapts to IF curvature, is applied on a slice-to-slice basis for more accurate results and also provides accurate delineation of the midline in the septum pellucidum, which is a source of failure for traditional approaches. The method is compared to two state-of-the-art methods for midline estimation and is validated using 75 imaging volumes (~3,000 imaging slices) acquired from 38 centers of subjects with dementia and vascular disease. The proposed method yields the lowest average error across all metrics: Hausdorff distance (HD) was 0.32 ± 0.23, mean absolute difference (MAD) was 1.10 ± 0.38 mm and volume difference was 7.52 ± 5.40 and 5.35 ± 3.97 ml, for left and right hemispheres, respectively. Using the proposed method, the midline was extracted for 5,360 volumes (~275K images) from 83 centers worldwide, acquired by GE, Siemens and Philips scanners. An asymmetry index was proposed that automatically detected outlier segmentations (which were <1% of the total dataset). Using the extracted hemispheres, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and significant differences in biomarker means were found across SCI and MCI and SCI and AD.

Evaluating the effect of a post-processing algorithm in detection of annular fissure on MR imaging.

Visualization of annular fissures on MRI is becoming increasingly important but remains challenging. Our purpose was to test whether an image processing algorithm could improve detection of annular fissures. In this retrospective study, two neuroradiologists identified 56 IVDs with annular fissures and 97 IVDs with normal annulus fibrosus in lumbar spine MRIs of 101 patients (58M, 43 F; age ± SD 15.1 ± 3.0years). Signal intensities of diseased and normal annulus fibrosus, and contrast-to-noise ratio between them on sagittal T2-weighted images were calculated before and after processing with a proprietary software. Effect of processing on detection of annular fissures by two masked neuroradiologists was also studied for IVDs with Pfirrmann grades of ≤ 2 and > 2. Mean (SD) signal baseline intensities of diseased and normal annulus fibrosus were 57.6 (23.3) and 24.4 (7.8), respectively (p < 0.001). Processing increased (p < 0.001) the mean (SD) intensity of diseased annulus to 110.6 (47.9), without affecting the signal intensity of normal annulus (p = 0.14). Mean (SD) CNR between the diseased and normal annulus increased (p < 0.001) from 11.8 (14.1) to 29.6 (29.1). Both masked readers detected more annular fissures after processing in IVDs with Pfirrmann grade of ≤ 2 and > 2, with an apparent increased sensitivity and decreased specificity using predefined image-based human categorization as a reference standard. Image processing improved CNR of annular fissures and detection rate of annular fissures. However, further studies with a more stringent reference standard are needed to assess its effect on sensitivity and specificity.

Development of tongue fissure detection system by examining the conditions in photometric stereo

Development of tongue fissure detection system by examining the conditions in photometric stereo

Detection of shallow underground fissures by time-frequency analysis of Rayleigh waves based on wavelet transform

Ground fissure is a geological hazard that poses a great threat to human life and, property and the environment. Therefore, it is necessary to detect shallow underground fissures effectively. In this paper, a time-frequency analysis of Rayleigh waves based on the wavelet transform is proposed to detect shallow underground fissures. The arrival time of the directed Rayleigh waves and the diffracted Rayleigh waves from the underground fissure is extracted from the time-frequency spectrum of any two traces. Furthermore, the locations of the underground fissures are calculated according to the time difference relation. Four sets of fracture models and one set of field data were used to test the effectiveness of the wavelet transform of Rayleigh waves. Moreover, the detection results of the actual data are compared with that of the high-density electrical method to further prove its detection effect. The field investigation shows that using the wavelet transform of Rayleigh waves to detect shallow underground fissures is feasible and effective.

Diagnostic Drama. Use of ICDAS II and Fluorescence-Based Intraoral Camera in Early Occlusal Caries Detection: A Clinical Study.

Background: Early diagnosis of occlusal caries is of paramount importance for a minimally invasive approach in dentistry. The aim of the present in vivo clinical prospective study was to compare the diagnostic outcomes of visual subjective evaluation between the International Caries Detection and Assessment System (ICDAS-II) and an intraoral fluorescence-based camera (VistaCam iX Proof, Dürr Dental, Bietigheim-Bissingen, Germany) for the detection of pits and fissures in early caries lesions of posterior teeth. Methods: The study included 1011 posterior teeth in 255 patients aged 13–20 years (mean age 16 ± 2.2 years). Two blinded operators evaluated all the occlusal surfaces and the first assigned an ICDAS-II code, while the second assessed the VistaCam score: sound enamel (score 0–1.2); initial enamel decay (score 1.2–1.5); dentine caries (score 1.5–3). Results: Some 283 (28%) of the assessed teeth were ICDAS-II code 0; 334 (33%) code 1; 189 (18.7%) code 2; 176 (17.4%) code 3; and 29 (2.9%) code 4. The level of agreement between the two procedures was expressed by using Cohen’s and Fleiss’ kappa statistics and performing McNemar’s test. VistaCam assessed in 513 (50.7%) sound enamel; in 292 (28.9%) initial enamel decay; and in 206 (20.4%) dentine caries. Conclusions: This comparative study showed a poor agreement between the two diagnostic methods, especially between ICDAS-II 0, 1 and 2 codes and fluorescence assessments.

An open-source framework for pulmonary fissure completeness assessment

We present an open-source framework for pulmonary fissure completeness assessment. Fissure incompleteness has been shown to associate with emphysema treatment outcomes, motivating the development of tools that facilitate completeness estimation. Generally, the task of fissure completeness assessment requires accurate detection of fissures and definition of the boundary surfaces separating the lung lobes. The framework we describe acknowledges a) the modular nature of fissure detection and lung lobe segmentation (lobe boundary detection), and b) that methods to address these challenges are varied and continually developing. It is designed to be readily deployable on existing lung lobe segmentation and fissure detection data sets. The framework consists of multiple components: a flexible quality control module that enables rapid assessment of lung lobe segmentations, an interactive lobe segmentation tool exposed through 3D Slicer for handling challenging cases, a flexible fissure representation using particles-based sampling that can handle fissure feature-strength or binary fissure detection images, and a module that performs fissure completeness estimation using voxel counting and a novel surface area estimation approach. We demonstrate the usage of the proposed framework by deploying on 100 cases exhibiting various levels of fissure completeness. We compare the two completeness level approaches and also compare to visual reads. The code is available to the community via github as part of the Chest Imaging Platform and a 3D Slicer extension module.

Using 3D seismic exploration to detect ground fissure

As a kind of supergene geological phenomenon, ground fissure has brought great inconvenience to human life. In addition, it also has a close relationship with earthquake. However, it is very difficult to ascertain the extension depth of ground fissure since its concealment and uncertainty. In this paper, 3D seismic exploration is used to detect ground fissure in Shanxi Province of China. Specific parameters for seismic data acquisition, processing and interpretation are analysed. Firstly, seismic data acquisition method and its corresponding parameters are discussed. Small dose explosive sources and high frequency geophones are used. Small trace interval and appropriate fold are also adopted. Secondly, seismic data processing is processed from shot record to seismic profile. Multi-domain loop iteration de-noising is used to get high signal-to-noise ratio data. Accurate near surface model, interactive iteration and residual static correction are used to eliminate the impact of low velocity zone and the static correction problem. Large common middle point bin and small velocity analysis interval are used for high accuracy velocity spectrum analysis. The mute parameter of stretching distortion and the migration aperture are researched for shallow ground fissure detection. Thirdly, seismic data interpretation is processed to get ground fissure distribution. Fault enhanced filter is used to improve the signal-to-noise ratio effectively and the chimney cube is used to identify ground fissure automatically. Thus, the specific 3D seismic exploration method used in this paper is suitable for ground fissure detection. Cited as : Shi, S., Liu, Z., Feng, J., Feng, G., Li, M. Using 3D seismic exploration to detect ground fissure. Advances in Geo-Energy Research, 2020, 4(1): 13-19, doi: 10.26804/ager.2020.01.02

Fissure Depth and Caries Incidence in First Permanent Molars: A Five-Year Follow-Up Study in Schoolchildren.

This study aimed to evaluate the association between the fissure depth (tooth morphology) of permanent molars and dental caries incidence with a five-year follow-up period. In total, 110 Mexican schoolchildren aged seven years were recruited at baseline, of which 88 children completed the follow-up assessments. The fissure depths of the first permanent lower molars were recorded, and dental caries [decayed, missing, and filled deciduous surfaces (dmfs) and decayed, missing, and filled permanent surfaces (DMFS)] was evaluated annually. A generalized estimating equation model was constructed for evaluating the association between dental caries and fissure depth. The initial and final DMFS scores were 0.02 (±0.2) and 1.55 (±2.49), respectively. The generalized estimating equation model showed that children with deep molar fissures were more likely to develop caries lesions, (Odds Ratio OR = 3.15, p = 0.028) compared to children with shallow fissures. Moreover, dental caries in primary teeth (OR = 1.07, p = 0.005) was associated with the development of carious lesions in the permanent dentition. Fissure depth was a useful predictor of dental caries, according to this five-year follow-up study. The detection of deep occlusal fissures in the lower first permanent molars could contribute to the identification of children at high risk of dental caries. Tooth morphology may be used to identify children at a higher caries risk, particularly in settings with limited resources.

An Efficient Method for the Detection of Oblique Fissures from Computed Tomography images of Lungs.

Detection of a pulmonary fissure in lungs is difficult due to its anatomical changeability among humans and it is essential in the clinical environment for accurate localizing and treating the lung abnormalities on a lobe level in human lungs. In this work, an algorithmic approach is proposed to detect the lung oblique fissures from lung computed tomography (CT) images. In the preprocessing module of our approach, the lung structures are enhanced using morphological operation and lung images are de-noised using Wiener filter. In the second module, lung regions are segmented using techniques, namely, thresholding and background subtraction. In the third module of our algorithm, initially, fissure regions are segmented using the active contour model, then by applying the rule based approach on the fissure regions, the oblique fissures are segmented. The proposed algorithm has been tested on 50 images collected from Lung Image Database Consortium (LIDC) and 30 images obtained from Early Lung Cancer Action Program (ELCAP).