Knee Joint Articular Cartilage Segmentation

International Journal of Computer Applications (0975 – 8887) Volume 42– No.19, March 2012

Knee Joint Articular Cartilage Segmentation, Visualization and Quantification using Image Processing Techniques: A Review M. S. Mallikarjuna Swamy

Mallikarjun S. Holi

Research Center, Dept. of Biomedical Engineering Bapuji Institute of Engineering and Technology Davangere -577004, India

ABSTRACT Knee is a complex and articulated joint of the body. Cartilage is a smooth hyaline spongy material between the tibia and femur bones of knee joint. Cartilage morphology change is an important biomarker for the progression of osteoarthritis (OA). Magnetic resonance imaging (MRI) is the modality widely used to image the knee joint because of its hazard free and high resolution soft tissue contrast. Cartilage thickness measurement and visualization is useful for early detection and progression of the disease in case of OA affected patients. A wide variety of algorithms are available for knee joint image segmentation. They are classified as pixel based and model based methods. Based on the human intervention required, segmentation methods are also classified as manual, semi-automatic and fully automatic methods. This paper reviews knee joint articular cartilage segmentation methods, visualization, thickness measurement, volume measurement and validation methods.

General Terms Medical image recognition

processing,

Computer

vision,

Pattern

Research Center, Dept. of Biomedical Engineering Bapuji Institute of Engineering and Technology Davangere -577004, India

Ligaments, which are extra capsular structures, are the strong band of connective tissue that connects one bone to another across a joint providing strength and stability to the joint. Articular Cartilage is a thin layer of high-quality, ultraslippery hyaline material which covers the ends of bones which slide against each other at the joint. Articular cartilage covers the patella, tibia and femur in the regions where the bones articulate with one another. Osteoarthritis (OA) is a most common disease of the knee joint affecting the elderly people. It occurs when cartilage becomes soft and get eroded due continuous wear and tear movements and with ageing. This often leads to inflammation, decrease in motion of joint due to stiffness, and formation of bone spurs (tiny growths of new bone). This decreases the ability of the cartilage to work as a shock-absorber to reduce the impact of stress on the joints. Fig.2 shows OA affected knee joint.

Exposed bone

Eroded cartilage

Keywords

Cruciate ligaments

Cartilage thickness, MRI, Osteoarthritis, Knee joint, Image segmentation.

1. INTRODUCTION Knee joint is the largest and most complex joint in human body. It is a hinge joint formed by the condyles of the femur, condyles of the tibia and posterior surface of the patella (knee cap). Intra-capsular structure of knee joint includes synovial membrane with synovial cavity filled with synovial fluid, articular cartilage, menisci (semi-lunar cartilages), cruciate ligaments, and burse. Fig.1 shows anatomy of knee joint.

Femur Capsule

Tendon

Synovial membrane

Patella Articular cartilage Tendon

Meniscus Tibia Fig.1 Knee joint anatomy

Bone spurs

Fig.2 OA affected knee joint The remaining cartilage wears down faster, and eventually, the cartilage in some regions may disappear altogether, leaving the bones to rub against one another during motion. It is at this stage that bone spurs may form. With OA, synovial fluid does not provide proper lubrication, which leads to pain, inflammation and restriction of movements at the joints. In severe OA there will be complete breakdown of cartilage over a period of time, leading to severe pain and joint loss. There is no artificial material that can replace only the cartilage at the joint. The OA affects about 21 million people in the United States. By the age 65, the majority of the population has radiographic evidence of OA and 11 percent have symptomatic OA of the

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International Journal of Computer Applications (0975 – 8887) Volume 42– No.19, March 2012 knee [1]. According to study [2] conducted on Indian population, OA was present in only 50.2% of the elderly aged 65-74 years, whereas it was 97.7% in elderly aged 84 years and above. Prevalence of OA increased as body mass index (BMI) increased. It was 51.36% amongst elderly with BMI less than 25, whereas it increased to 100% amongst elderly with BMI equal to or more than 40.

progression of OA. The 3D visualization gives complete information and insight on cartilage degradation and menisci tears. In specific cases, MR images with high signal to noise ratio with high resolution are used for in-vitro studies. But in most of the in-vivo, low contrast knee image slices are used. The 3D reconstruction algorithms are used to obtain the data missing in 2D images.

2. BACKGROUND

3. KNEE JOINT SEGMENTATION METHODS

MRI can image cartilage, bone and other surrounding soft tissues distinctly. MRI is a non-invasive and repetitive imaging study of an individual is possible without side effects. The assessment of cartilage dimensions is important for the study of the progression of cartilage damage due to OA. MRI images are widely used for diagnosis of knee joint abnormalities. This imaging modality can be used in in-vivo and in-vitro studies of anatomical structures. MRI provides knee joint images in sagittal, coronal and axial planes. Sagittal images give better view of cartilage than the other two. Fig.3 shows the MR images of knee joints (a) normal (b) OA affected.

Image segmentation is defined as portioning of an image into non overlapping constituent regions that are homogeneous with respect to some characteristic such as intensity or texture. A segmentation method finds those set that correspond to distinct anatomical structures or regions of interest in the image [3]. If the domain of the image is given by Ω then the segmentation problem is to determine the sets 𝑆𝑘 ∁ 𝛺 whose union is the entire domain Ω. Thus the sets that make up segmentation must satisfy 𝛺=

𝐾 𝑘=1 𝑆𝑘

(1)

Where𝑆𝑘 ∩ 𝑆𝑗 = ∅ 𝑓𝑜𝑟 𝑘 ≠ 𝑗 and each 𝑆𝑘 is connected.

(a)

Knee joint image segmentation is a very challenging task because of the complexity of joint. Knee joint segmentation methods can be classified into three categories based on manual intervention required, namely manual, semiautomatic and fully automatic. The manual segmentation methods are simple but laborious and time consuming for 3D reconstruction. Semi automatic methods are developed to reduce the manual intervention by automating few steps of processing. Fully automatic methods are also developed with certain limitations. Knee Segmentation methods are also classified into two categories. Pixel or intensity based segmentation methods and model or geometry based segmentation methods. The edge detection, thresholding, region growing and region merging methods are pixel based techniques. On the other side snake algorithms, active contours, atlases and deformable models are geometry based.

3.1 Pixel or Intensity based Segmentation Methods

(b) Fig. 3. MR image of (a) Normal knee joint (b) OA affected knee joint High resolution gradient echo MRI sequences with fat suppression are the most useful techniques for quantization of cartilage dimensions. Even though, MRI directly visualizes the cartilage for progressive assessments, image processing techniques are needed for better visualization and quantification. Knee joint cartilage thickness measurement gives vital information in the diagnosis and treatment of

In the earlier edge detection method of Kshirsagar et. al. [4], Canny edge detection and template matching techniques are used. To locate the boundary of femur the edge map is cross correlated with standard template. Approximate centre of the femur is obtained. Mask is obtained to delineate the exact boundaries. They extended the method to 3D edge detection for automatic delineation of cartilage from 3D MR images of human knee joint. Canny edge detection technique is used recursively to segment the cartilage [5]. The Canny edge detection operator returns a value for the first derivative in the horizontal direction (Gx) and the vertical direction (Gy). From this the edge gradient and direction can be determined [6]. ∇𝑓 𝑥, 𝑦 =

𝜕𝑓 𝜕𝑥

,

𝜕𝑓 𝜕𝑦

(2)

The magnitude (edge strength) of the gradient is then approximated using the formula: |G| = |Gx| + |Gy|

and Θ = tan-1(Gy/Gx)

(3)

Cohen et al. [7] initially segment cartilage manually by digitizing the consecutive points along the articular contour curves with a typical spacing of 0.5-1.0 mm. To reduce the time, a semi automatic segmentation method is adopted.

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International Journal of Computer Applications (0975 – 8887) Volume 42– No.19, March 2012 Region of interest is obtained from each image as in the manual segmentation by fixing the points along the curves. An interpolated cubic B-spline curve is fitted for the points. The image gradient vector is evaluated using the Prewitt convolution kernel. The B-spline curve, which follows the contour of the desired surface, is then sampled at 0.5 mm intervals. A B-spline of degree n is derived through n convolutions of the box filter, B0. Thus, B1=B0*B0 denotes a B-spline of degree 1, yielding the familiar triangle filter. The B-spline of degree 1 is equivalent to linear interpolation. The second degree B-spline B2 is produced by convolving B0*B1. The cubic B-spline B3 is generated from convolving B0*B2. That is B3=B0*B0*B0*B0 [8]. The filter function is 𝑕 𝑥 =

1 6

3 𝑥 −6 𝑥 +4 − x|3 + 6 x|2 − 12 x + 8 0

0≤ 𝑥