contour-analysis

name	contour-analysis
description	Shape classification through mathematical ratios, bounding box extraction, centroid computation, and medical morphometric analysis
version	1.0.0
tags	["opencv","contours","shape-analysis","features","medical-imaging","morphometry"]

Contour Analysis

When to Use This Skill

• Counting objects in an image
• Measuring object area, perimeter, or physical dimensions
• Classifying shapes (round vs elongated, regular vs irregular)
• Medical image analysis (tumor shape assessment)
• Drawing bounding boxes or centroids on detected objects

Decision Framework

Shape Metric Selection

Metric	Formula	Range	Tells You
Aspect Ratio	width / height	0→∞	Shape elongation (1.0 = square/circle)
Extent	Object Area / Bounding Box Area	0→1.0	How much the box is filled
Solidity	Object Area / Convex Hull Area	0→1.0	Surface regularity (1.0 = smooth, <1.0 = irregular)
Eccentricity	Minor Axis / Major Axis	0→1.0	Circularity (0 = circle, 1 = line)

Medical Application — Tumor Shape Classification

Solidity value?
├── ≈ 1.0 (smooth, convex surface)
│   └── Likely BENIGN — regular, well-defined boundary
│
└── << 1.0 (irregular, spiculated surface)
    └── Likely MALIGNANT — irregular projections, infiltrative margin

Solidity is a gold-standard feature in medical image analysis for distinguishing benign vs malignant masses. Convex Hull wraps the object tightly — if the actual area is much smaller than the hull, the surface has indentations/spikes (suspicious morphology).

Contour Finding Parameters

Parameter	Recommended	Alternative	When
Mode	RETR_EXTERNAL	RETR_TREE	External = outer boundaries only. Tree = nested hierarchy
Method	CHAIN_APPROX_SIMPLE	CHAIN_APPROX_NONE	Simple = corner points only (saves memory). None = all boundary pixels

Critical Gotchas

1. Image MUST Be Binary Before findContours

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL,
                                cv2.CHAIN_APPROX_SIMPLE)

2. findContours Expects White Objects on Black Background

If your objects are dark on a light background, use THRESH_BINARY_INV to invert.

3. drawContours Index: -1 = All

cv2.drawContours(img, contours, -1, (0, 255, 0), 2)  # All contours
cv2.drawContours(img, contours, 0, (0, 255, 0), 2)   # Only first contour

4. Minimum Points for fitEllipse

cv2.fitEllipse() requires at least 5 points in the contour. Filter small contours first.

5. Physical Measurement Requires Calibration

Pixel area alone is meaningless in real units. You need:

Real Area (mm²) = Pixel Area × (Physical Size of 1 Pixel in mm)²

Quick Reference

Complete Contour Analysis Pipeline

import cv2
import numpy as np

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL,
                                cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours:
    area = cv2.contourArea(cnt)
    perimeter = cv2.arcLength(cnt, True)
    x, y, w, h = cv2.boundingRect(cnt)
    
    # Aspect Ratio
    aspect_ratio = w / h
    
    # Extent
    rect_area = w * h
    extent = area / rect_area if rect_area > 0 else 0
    
    # Solidity
    hull = cv2.convexHull(cnt)
    hull_area = cv2.contourArea(hull)
    solidity = area / hull_area if hull_area > 0 else 0
    
    # Centroid
    M = cv2.moments(cnt)
    if M['m00'] > 0:
        cx = int(M['m10'] / M['m00'])
        cy = int(M['m01'] / M['m00'])

Eccentricity via Ellipse Fitting

if len(cnt) >= 5:
    ellipse = cv2.fitEllipse(cnt)
    (center, (minor_axis, major_axis), angle) = ellipse
    eccentricity = minor_axis / major_axis  # 0=circle, 1=line

Scikit-Image Alternative (37 Features in One Call)

from skimage.measure import label, regionprops

labeled = label(binary_image)
for region in regionprops(labeled):
    print(region.area, region.perimeter, region.solidity,
          region.eccentricity, region.centroid, region.bbox)

skimage.regionprops extracts 37 morphometric features per object in a single call, compared to manual per-feature computation in OpenCV.