Image Processing Reference

In-Depth Information

FIGURE 3
Image binarization on crystallization trial images: (a) original images, (b) Green

percentile threshold (
p
= 95), and (c) Green percentile threshold (
p
= 99).

4.2 Region Features

After we generate the binary image, we apply connected component labeling to segment the

regions (crystals). The binary image can be obtained from any of the thresholding methods.

Let O be the set of the blobs in a binary image B, and B consists of
n
number of blobs. The

blobs are ordered from the largest to the smallest such that area (
O
i
) ≥ area(
O
i
+1
). Each blob

O
i
is enclosed by a minimum boundary rectangle (MBR) having width (
w
i
) and height (
h
i
). It

should be noted that the blobs may not necessarily represent crystals in an image. For such

cases, the blob features may not be particularly useful for the classifier.
Table 1
provides a list

of the region features. We extract five features (area, perimeter, filled area, convex area, and

eccentricity) from the three largest blobs
O
1
,
O
2
, and
O
3
. If the number of blobs is less than 3,

value 0 is assigned. We apply green percentile image binarization with
p
= 95 and
p
= 99. From

each binary image, we extract 17 region features. Thus, at the end of this step, we obtain 34

features.

Table 1

Region Features

Symbol

Term

Description

n

Number

of

Number of blobs with a minimum size of 25 pixels

blobs

ch

Convex

hull

Area of the convex hull (smallest set of pixels) that enclose all blobs

area

a
1
,
a
2
,
a
3

Blob area

Number of pixels in the three largest blobs
O
1
,
O
2
,
O
3

p
1
,
p
2
,
p
3

Blob perimeter

Perimeter of the three largest blobs
O
1
,
O
2
,
O
3

fa
1
,
fa
2
,
fa
3

Blob filled area

Number of white pixels in the three largest blobs
O
1
,
O
2
,
O
3

ch
1
,
ch
2
,
ch
3

Blob

convex

Number of pixels within the convex hull of the three largest blobs

O
1
,
O
2
,
O
3

area

e
1
,
e
2
,
e
3

Blob

eccentri-

Eccentricity of the three largest blobs
O
1
,
O
2
,
O
3

city

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