A prerequisite for any further processing of Pap smear images and deriving conclusions for the characterization of their content is an accurate determination of the surface of cell nuclei. These methods show remarkable success in segmentation of the structural parts of the cell.
Thresholding
These methods manage to exclude the image background and recognize cell locations and boundaries. In the following paragraphs, several detection and segmentation methods are categorized in terms of the general image processing method they are based on, for determining the boundaries of nuclei in Pap smear images.
Edge Detection
This results in the construction of an image with a bimodal-like intensity histogram, from which an optimal threshold can be obtained for the segmentation of the images. Then, the Sobel operator and the non-maximum suppression are used to extract the gradient image, which is binaryized by setting an upper and lower edge value limit.
Mathematical Morphology
In the second phase, the identification of the likely locations of the centers of gravity in the cell nuclei takes place. A morphology-based imaging process is proposed for the detection of the regional minima in the image, which indicate the existence of candidate nuclei of cells.
Pixel Classification
The K-means clustering algorithm classifies the pixels of the image in a first step into three categories (cytoplasm, nucleus and background). The method provides simultaneous segmentation of all cells in the image, by identifying the pixels of the cytoplasm, nucleus and background.
Template Matching
In Vaschetto et al. 2009), a three-color based algorithm combining color information, expert knowledge and fuzzy systems is proposed, with the aim of improving the accuracy of the method proposed in Sobrevilla et al. 2008) for detecting and segmenting the nuclei in Pap smear images. Finally, a modified seed-based region growing (MSBRG) algorithm for automated cervical cell segmentation was proposed in Mustafa et al.
Deformable Models
In the boundaries of the predefined search space, the most probable locations for the points of the core boundaries are defined, according to a Viterbi search-based dual active contour algorithm. The same procedure is followed for the delimitation of the nuclear boundaries, which are also initially approximated by a circle.
Segmentation of Overlapping Nuclei in Cervical Images
In recent years, some efforts have been made to achieve reproducible and objective characterization of the Pap smear images through computer vision, reducing the dependence on human experts. To this extent, several commercial interactive systems aimed at the automatic classification of the Pap smear images have been developed.
Features of Cervical Cells Used for Classification
The features used in these methods relate to both the intensity and shape characteristics of the nucleus and cytoplasm. So in Marinakis et al. 2009), a technique based on a genetic algorithm is proposed for the selection of the best performing subset of features.
Classification Techniques
The effectiveness of the proposed diagnostic system has been demonstrated using 550 annotated Pap smear images. The accurate segmentation of the core area is a prerequisite for deriving diagnostic conclusions and characterizing the contents of Pap smear images.
Local Control Rules
With the derivative control, the change is proportional to the derivative of the error with respect to a unit change in time¯i(t)– e¯i(t-1). Each CA has in principle three possibilities to converge and to reach the remodeling equilibrium: (1) the bone is completely resorbed (r¼rmin);. 2) the bone reaches the maximum value of the density (r¼rmax); or (3) the bone has an apparent density that satisfies Eq.5en16metDri¼0 and therefore SED¼SED* (Weinans et al.1992).
Finite Element Analysis (FEA)
The idea of using the influence of CAineighbourhoods can be thought of as a filtering technique that prevents the appearance of checkerboard patterns and grid dependencies (Patel et al. 2008). The saturation density rmax was assumed to avoid a density value greater than that of bone tissue (Carter et al. 1989).
Cost Indices
Local control rules allowed updating the relative mass, Equation 16, or the Young's modulus, Equation 17, and evaluating the corresponding stress state. The PID gains (cP, cI, cD) in Equation 16 or 17, the target (SED*) in the error signal (ei(t)=SEDi– SED*) and the normalized total mass and energy multiplier in Equation 20 were optimized by minimizing the cost indices J1, Eq.20 or J2, Eq.21, as explained in the next subsection.
Non Linear Constrained Minimization Problem
Under the same initial conditions, the corresponding evolutions of the cost indices J1 and J2 are shown in Fig. In conclusion, Figure 8 shows the configurations of the initial and final relative mass distributions of the trabecular structure.
Methods for the 3D Modelling
The patient was scanned in the supine position from the pelvic girdle to the distal end of the femur. A point on the femur area was clicked and the program then started calculating the new segmentation.
Generation of Surface Mesh
A threshold based on the Hounsfield scale was used to separate the cortical bone of the femur and acetabulum from the surrounding muscles and tissues. This means that all points on the thigh area connected to the selected point were used to create a new mask.
Surface Mesh to Volumetric Mesh
The amount of detail of the femur was reduced by conservatively smoothing it without compromising the fidelity of the model. Afterwards, an autoremesh was performed with a maximum edge length of the triangle limited to 5 mm.
Instancing and Location of the Hip Joint Centre
The femoral head acted as a slave surface mesh while the acetabulum behaved as a master surface mesh to respect the master and slave formulation in Abaqus. In addition, the femoral head was smoothed and refined relative to the pelvic girdle to prevent any sharp corners in the model that could cause convergence problems and to prevent any major node penetrations into the slave surface from going undetected (Abaqus 6.9) (as discussed in Section 2.6).
Material Property of the Finite Element (FE) Model
Once the surface meshes of the femur and pelvic girdle satisfactorily passed the mesh quality test, they were saved as an “inp” file format and exported separately to Abaqus. An equivalent bone material property was defined for a finite element (FE) model with an average modulus of elasticity of 750 MPa and an average density of 1.281 g/cm3.
Contact Algorithm and Boundary Conditions
It has also been reported that the most critical movement in femoroacetabular impingement is internal rotation of the hip in 90 flexion (Ganz et al. 2008; Tannast et al. 2007b). 9 (a) The center of rotation was linked (arrow) to the femur. (b) The pelvic wall was secured at the angles (arrow) with non-rigid bone support of the acetabulum.
Kinematics of Impingement
Location of Impingement
The kinematics of impingement were found to decrease with thinning of the articular cartilage. However, there was a shift in the zone of impingement and maximum bone-to-bone contact with thinning of the articular cartilage.
MRI Image Acquisition
The workflow in creating the FEA model is summarized in Fig.3. The MRI image slices are then imported into solid modeling software to reconstruct the geometry of the tibio-femoral joint contact area.
Construction of 3D Model
Figure 7 shows a pseudo-color image obtained from MRI, where the blue area between the femur and tibia can be identified as the menisci. The geometries were constructed by controlled tracing of the boundaries of each component on each slice, and then a solid body was created.
Processing of Solid Model
Haut Donahue TL et al (2002) Moodeela elementii xumuraa kan jilba namaa qorannoo tuttuqaa tibio-femoral. Peña E et al (2005) Xiinxala elementii xumuraa dhiibbaa imimmaan meniscal fi meniscectomies baayoomakaaniksii jilba namaa irratti qabu.
Finite Element Simulation: Model1
Finally, the mechanical properties of the materials used in the simulation are reported in Table 1. In the validation step of the present study, a CAD model was constructed. Table 1 Material properties.
Finite Element Simulation: Model2
Stair climbing has been confirmed to be a critical task for the primary stability of the prosthetic femur. Based on the above studies and with the aim of evaluating the effects of stem positioning in a configuration close to the actual condition (Viceconti et al. 2001), in this study we designed a second model called “Model2” consisting of : the same femoral stem used for Model1 – i.e.
Numerical Simulations
In particular, the 5 kN compressive load associated with the critical configuration provided a Von Mises stress value greater than the maximum yield stress for the stem. The maximum value of the first principal stress on the stem is 593 MPa, and the value of the z-component in compression increases to 727 MPa.
Fatigue Experimental Test
Figure 6 shows the Von Mises stress distribution in the critical configuration; the place of maximum value is marked. At the end of the mechanical test, the stem was pulled out of the cement jacket in which it was embedded.
Model1: Numerical Simulation and Fatigue Experimental Test
Comparison Between Model1 and Model2
Rather, the rationale behind the development of Model2 was the attempt to evaluate the consequences, in a configuration closer to a clinical setting, of the stem positioning individually through Model1 FEM simulations. Specifically, Model2 represents the usual clinical configuration of the hip replacement implant in an ideal functioning environment, i.e.
Limitations of the Study and Future Plans
Abnormalities in the angular position of the stem—within the angular variations examined in the present study, of course—as for both hip adduction and flexion do not change the most stressed areas in either model; however, interesting changes can be observed in the variation of voltage values. This means that some misalignment during surgical implantation may cause some acceleration in the loss of primary stability, but, more importantly, is responsible for more rapid damage to the entire implant once stability is lost.
The 2010 Revision of the International Standard ISO 7206-4
Mechanical tests are almost ready to verify how close the numerical simulations are to the mechanical behavior of the femoral stem. Reggiani B, Cristofolini L, Taddei F, Viceconti M (2008) Sensitivity of the primary stability of the cementless hip stem to its position and orientation.
Virtual Landmark Identification
These measurements can serve as a guide to distinguish dysplastic from normal morphology (Delaunay et al. 1997). Finally, various landmarks can be used to determine insertion sites in ligament reconstruction (Sch€ottle et al. 2007; Ziegler et al. 2010).
Automatic Landmark Extraction
Predicting the shape of one bone from observing another from the same joint (Yang et al.2008). It helps detect shape-related pathologies (eg, cam impingement, osteoarthritis), sex-related anatomical differences, and aging (Styner et al. 2005).
Basic Concepts
This is a somewhat arbitrary value that allows for a compact model while capturing the most variance in the data. These two steps are fundamental, because incorrect matches can either introduce too much variation or lead to incorrect examples of the model (Dryden and Mardia1998; Lamecker et al.2004).
Alignment and Non-rigid Registration
Non-rigid bone registration is based on the method introduced by Rueckert et al. Then, the T-transform FFD can be written as the 3-D tensor product of 1D B-cubic lines (Eqs. 2,3, and 4).
Illustrative Results: Femur Bone Main Modes of Shape Variation
The deformation is affected by the distance between the control points: a larger distance leads to a smoother global shape, while a relatively smaller distance leads to higher local deformation. 4 Average shape and variations of the shape of the full femur for the first three conditions, based on an SSM built from 43 training femurs.
Patient-Specific Surgical Guides
The surgical guides are then designed so that the fixation pins of the cutting blocks are drilled through the guides. The evolution of planning, moving from 2D to X-rays (as has often been done before) Fig. 7 Illustration of the patient-specific Signature™ femoral guide for total knee replacement. a) Guide fitting on the femur bone model.
Computer-Assisted Surgical Planning and Navigation
These limits are plotted in fig. 6 together with the measured data as a function of mineral volume fraction and total porosity (given as Dfw+f). Martinus Nijhoff, Dordrecht Nikolov S, Raabe D (2008) Hierarchical modeling of the elastic properties of bone at the submicron.
Materials and Methods
The 3D volume measurement is based on the marching cube volume model of the binarized objects within the VOI. In the post-extractive side of each patient, one of the biomaterials (test sample) used in this study was implanted, while the opposite side was treated only with blood clot (control sample).
Results
A different color was chosen for each volume to distinguish structures with different densities in the 3D image of the sample: yellow represents the less radioactive component corresponding to bone, orange represents areas that have reached the first stage of calcification, and finally red represents areas that have reached higher degree of calcification (Figure 2). 2 An example of the 3D structure of a sample grafted with biomaterial 2 and a color legend to explain the different radiopacities of the sample.
Discussion
10 Top image shows 3D reconstruction color images of biomaterial 3 test sample (all sample on the left and a piece of the same sample on the right), while the bottom shows 3D reconstruction color images of biomaterial 3 control sample (all sample on the left and a piece of the same sample to the right). Despite the undoubted advantages of the microtomographic technique compared to the traditional optical microscopy, only histological analysis has been able to confirm the presence of biomaterial scaffold residues in the biomaterial 3 test bone sample, probably due to lower radiopacity and fine powder formulation.
Conclusions
Mechanical causes can be identified by the presence of fixture or abutment fracture, screw loosening or loosening, and fracture of ceramic or metal superstructure prostheses (Albrektsson1988; Oh et al.2002). In fact, new leakage tests showed the impossibility of ensuring a complete seal (Rack et al. 2010; Zipprich et al. 2007; Tsuge et al. 2008).
Materials and Methods
Only one study today demonstrates the possibility of directly observing the in vitro microgap attachment of conical connections by means of hard X-ray synchrotron radiation, because in the past many authors believed that a total seal was assured by this connection geometry (Rack et al. al.2010). 14 Microtomographic horizontal (a) and vertical (b) section of sample 1 with its Rx image (left corner) and indication of the L0 level.
Results
For tapered connections, sequential analysis of all reconstructed axial sections determined that L0 identifies the initial contact between the implant and the abutment, while L1 identifies the section where a microgap can be observed between two adjacent surfaces, at the end of the connection seal (Figures 3 and 4). With the CTan software, it was also possible to measure the side surface of the truncated cone between L0 and L1, which indicates the contact surface between the components.
Discussion
Many authors wrote about the importance of the conditions at the interface between fixture and abutment at the level and loss of surrounding bone tissue (Ricomini Filho et al.2010; Broggini et al.2006). This means that the risk of bacterial infiltration still cannot be completely avoided today (Ricomini Filho et al.2010; Mangano et al.
Conclusions
Dibart S, Warbington M, Su MF, Skobe Z (2005) In vitro evaluation of the implant-abutment bacterial seal: the locking conical system. Jansen VK, Conrads G, Richter EJ (1997) Microbial leakage and marginal adaptation of the implant-abutment interface.
Materials and Methods
This was found to be an effective way to reduce spurious wave reflections at the output boundary of the 3D model. Also, a scheme for coupling a 3D model with a digital implementation of the Westerhof model (both in the frequency and time domains) has been implemented (Westerhof et al.1969).
A Multi-Scale Model of the Left Ventricle
As shown in Equation 7, mechanical strength is obtained by deriving the energy equation of the muscle in terms of attached cross bridges (EX) and muscle fiber length (lm). Full details of the formulation and assumptions are available in (Dı´az-Zuccarini and LeFe`vre2007).
Computational Model of the Mitral Valve and Coupling with the LV Model
At each time step, drag,f*pz and lift,f*Py, forces exerted by the flowing fluid at an arbitrary point P on the airfoil surface are reduced to the center of gravity, G. Conservation of kinetic momentum is imposed, resulting in the dynamic equation that describes the motion.
Results and Discussion
Negative pressure values vary on the surface of the leaflet depending on the direction of movement (Fig. 5). Inflating the balloon deploys the valve stent and anchors it within the old dysfunctional valve (Fig. 6c).
Materials and Methods
In addition, the RVOT volumes were divided into sections and the shape of the cross-sections standardized into circles. In addition, 12 patients underwent four-dimensional (4D) CT imaging to obtain 3D information in ten frames of the cardiac cycle (Schievano et al. 2011).
Results
The models also allowed for trial implantation of the PPVI device (Figure 15b) to test the correct figure. FE analyzes allowed the evaluation of the stresses not only in the stent, but also of the stresses induced in the RVOT wall by device deployment.
Discussion
Schievano S, Migliavacca F, Coats L, Khambadkone S, Carminati M, Wilson N, Deanfield J, Bonhoeffer P, Taylor A (2007a) Rapid prototyping of three-dimensional modeling of the right ventricular outflow tract and pulmonary trunk from magnetic resonance data - clinical utility for percutaneous pulmonary valve implantation. Computational fluid dynamics methodology allows to analyze the fluid flow in each point of the kayak model.
Geometry and Computational Domain
It is a general principle of boat hydrodynamics that a boat's speed will be a function of the amount of force exerted and the amount of resistance created by the water as the boat's hull passes through it. The numerical simulations were performed only for the outer shell section of the kayak hull geometry, assumed to be submerged in still water.
Governing Equations
To obtain the geometry of each kayak, the models were developed in Gambit, a geometry modeling program of the commercial software Ansys FluentTM6.3 (Ansys, Canonsburg, Pennsylvania, USA), which provides sophisticated computational fluid dynamics software (Figure 2). These surfaces were then meshed using the same program, creating a mesh that was imported into the Fluent fluid dynamics computer program for analysis (Figure 3).
